# kernel used
from platform import python_version
print('The version of ',python_version())
The version of 3.6.8
# importing the data-analysis and visualization libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
%matplotlib inline
import os
import requests
import re
import geopy.distance
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
def download_data(url, file_name):
'''
input :
1. url - the url from where the file has to be downloaded
2. file_name - the name with with the corresponding file has to be created
'''
if file_name not in os.listdir():
t0 = datetime.now()
response = requests.get(url)
with open(os.path.join(os.getcwd(), file_name), 'wb') as f:
f.write(response.content)
print('****** File ', file_name,', of size :',os.path.getsize(file_name)/1000000 ,' MB sucessfully downloaded in ',
str((datetime.now()- t0).seconds),' seconds !!!!.')
else:
print('****** File with name ',file_name,' already exists. Please verify.')
# training-set values
url = 'https://s3.amazonaws.com/drivendata/data/7/public/4910797b-ee55-40a7-8668-10efd5c1b960.csv'
file_name = 'raw_train_feature_vectors.csv'
download_data(url, file_name)
****** File with name raw_train_feature_vectors.csv already exists. Please verify.
# training-set labels
url = 'https://s3.amazonaws.com/drivendata/data/7/public/0bf8bc6e-30d0-4c50-956a-603fc693d966.csv'
file_name = 'raw_train_target_vectors.csv'
download_data(url, file_name)
****** File with name raw_train_target_vectors.csv already exists. Please verify.
# testing-set values
url = 'https://s3.amazonaws.com/drivendata/data/7/public/702ddfc5-68cd-4d1d-a0de-f5f566f76d91.csv'
file_name = 'raw_test_feature_vectors.csv'
download_data(url, file_name)
****** File with name raw_test_feature_vectors.csv already exists. Please verify.
# train data-set
train_data = pd.read_csv('raw_train_feature_vectors.csv')
print('There {} features and {} data-points in train-data set.'.format(train_data.shape[1],train_data.shape[0]))
There 40 features and 59400 data-points in train-data set.
# get the data-types of the features
train_data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 59400 entries, 0 to 59399 Data columns (total 40 columns): id 59400 non-null int64 amount_tsh 59400 non-null float64 date_recorded 59400 non-null object funder 55765 non-null object gps_height 59400 non-null int64 installer 55745 non-null object longitude 59400 non-null float64 latitude 59400 non-null float64 wpt_name 59400 non-null object num_private 59400 non-null int64 basin 59400 non-null object subvillage 59029 non-null object region 59400 non-null object region_code 59400 non-null int64 district_code 59400 non-null int64 lga 59400 non-null object ward 59400 non-null object population 59400 non-null int64 public_meeting 56066 non-null object recorded_by 59400 non-null object scheme_management 55523 non-null object scheme_name 31234 non-null object permit 56344 non-null object construction_year 59400 non-null int64 extraction_type 59400 non-null object extraction_type_group 59400 non-null object extraction_type_class 59400 non-null object management 59400 non-null object management_group 59400 non-null object payment 59400 non-null object payment_type 59400 non-null object water_quality 59400 non-null object quality_group 59400 non-null object quantity 59400 non-null object quantity_group 59400 non-null object source 59400 non-null object source_type 59400 non-null object source_class 59400 non-null object waterpoint_type 59400 non-null object waterpoint_type_group 59400 non-null object dtypes: float64(3), int64(7), object(30) memory usage: 18.1+ MB
# Casting the data types of date fileds to datetime dtype
train_data['date_recorded'] = pd.to_datetime(train_data['date_recorded'])
#train_data['construction_year'] = pd.to_datetime(train_data['construction_year'])
#train_data.isna().sum()
for a in train_data.columns[train_data.isna().any()].tolist():
print(a ,' has ',train_data[train_data[a].isna()].shape[0],' missing values')
funder has 3635 missing values installer has 3655 missing values subvillage has 371 missing values public_meeting has 3334 missing values scheme_management has 3877 missing values scheme_name has 28166 missing values permit has 3056 missing values
# Count of types of features
categorical = len(train_data.select_dtypes(include='object').columns)
integ = len(train_data.select_dtypes(include='int64').columns)
float_num = len(train_data.select_dtypes(include='float64').columns)
date_time = len(train_data.select_dtypes(include='datetime64[ns]').columns)
categorical, integ, float_num, date_time
(29, 7, 3, 1)
dty_list =[]
for a, b in zip([categorical, integ, float_num, date_time],['categorical', 'integ', 'float_num', 'date_time']):
for _ in range(a):
dty_list.append(b)
plt.hist(dty_list, color = 'green')
plt.xlabel('\n Data types of the features')
plt.ylabel('Counts')
plt.title(' Comparison of the fetaures by dtypes')
Text(0.5, 1.0, ' Comparison of the fetaures by dtypes')
# Start dissecting the categorical features
[ col for col in train_data.columns if train_data[col].dtypes == 'object']
['funder', 'installer', 'wpt_name', 'basin', 'subvillage', 'region', 'lga', 'ward', 'public_meeting', 'recorded_by', 'scheme_management', 'scheme_name', 'permit', 'extraction_type', 'extraction_type_group', 'extraction_type_class', 'management', 'management_group', 'payment', 'payment_type', 'water_quality', 'quality_group', 'quantity', 'quantity_group', 'source', 'source_type', 'source_class', 'waterpoint_type', 'waterpoint_type_group']
fund_30 = pd.DataFrame(train_data['funder'].value_counts().head(30))
ax = fund_30.T.plot(kind='bar', figsize = (18, 10) , grid = True )
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))
install_30 = pd.DataFrame(train_data['installer'].value_counts().head(30))
ax = install_30.T.plot(kind = 'bar' , figsize = (16, 10))
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))
wpt_30 = pd.DataFrame(train_data['wpt_name'].value_counts().head(30))
ax = wpt_30.T.plot(kind = 'bar' , figsize = (17, 10))
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))
len(train_data['basin'].unique())
9
# checking if there are missing values for basin
train_data['basin'].isna().sum()
0
# Let's visualize , how basins compare in terms of supplying the water
basin_df = pd.DataFrame(train_data['basin'].value_counts())
ax = basin_df.T.plot(kind = 'bar', figsize=(17,7))
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))
#train_data['subvillage'].value_counts()
train_data['subvillage'].isna().sum()
371
len(train_data['subvillage'].unique())
19288
'Lake Victoria' = Latitude: -1° 00' 0.00" S Longitude: 33° 00' 0.00" E
'Pangani' = Latitude: -5° 25' 30.94" S Longitude: 38° 58' 29.03" E
'Rufiji' = Latitude: -8° 00' 0.00" S Longitude: 39° 19' 60.00" E
'Internal'(same as ruvu) = Latitude: -6° 48' 17.99" S Longitude: 38° 41' 59.99" E
'Lake Tanganyika' = 6.2556° S, 29.5108° E
'Wami' - Latitude: -6° 07' 60.00" S Longitude: 38° 48' 59.99" E
'Lake Nyasa' = 11.6701° S, 34.6857° E
'Ruvuma' = 10.6879° S, 36.2631° E
'Lake Rukwa' = 8.0167° S, 32.2654° E
train_data['basin'].value_counts()
Lake Victoria 10248 Pangani 8940 Rufiji 7976 Internal 7785 Lake Tanganyika 6432 Wami / Ruvu 5987 Lake Nyasa 5085 Ruvuma / Southern Coast 4493 Lake Rukwa 2454 Name: basin, dtype: int64
train_data['region'].value_counts()
Iringa 5294 Shinyanga 4982 Mbeya 4639 Kilimanjaro 4379 Morogoro 4006 Arusha 3350 Kagera 3316 Mwanza 3102 Kigoma 2816 Ruvuma 2640 Pwani 2635 Tanga 2547 Dodoma 2201 Singida 2093 Mara 1969 Tabora 1959 Rukwa 1808 Mtwara 1730 Manyara 1583 Lindi 1546 Dar es Salaam 805 Name: region, dtype: int64
region_data = pd.DataFrame(train_data['region'].value_counts())
ax = region_data.T.plot(kind = 'bar', figsize= (16,9))
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))
print('Number of LGA in the dataset : ',len(train_data['lga'].unique()),
' and number of missing entries is ',train_data['lga'].isna().sum())
Number of LGA in the dataset : 125 and number of missing entries is 0
print('Number of regions in the dataset : ',len(train_data['region'].unique()),
' and number of missing entries is ',train_data['region'].isna().sum())
Number of regions in the dataset : 21 and number of missing entries is 0
agg_reg_lga = train_data[['region','lga']].groupby('region').count()
ax = agg_reg_lga.T.plot(kind = 'bar', figsize=(17, 10))
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))
plt.title(' Regions by number of sub-regions ')
plt.ylabel(' Count of lga ')
Text(0, 0.5, ' Count of lga ')
print('Number of wards in the dataset : ',len(train_data['ward'].unique()),
' and number of missing entries is ',train_data['ward'].isna().sum())
Number of wards in the dataset : 2092 and number of missing entries is 0
location_df = train_data[['region','lga','ward']].groupby(['region','lga']).count()
location_df
| ward | ||
|---|---|---|
| region | lga | |
| Arusha | Arusha Rural | 1252 |
| Arusha Urban | 63 | |
| Karatu | 326 | |
| Longido | 310 | |
| Meru | 1009 | |
| Monduli | 189 | |
| Ngorongoro | 201 | |
| Dar es Salaam | Ilala | 497 |
| Kinondoni | 93 | |
| Temeke | 215 | |
| Dodoma | Bahi | 224 |
| Chamwino | 347 | |
| Dodoma Urban | 358 | |
| Kondoa | 523 | |
| Kongwa | 361 | |
| Mpwapwa | 388 | |
| Iringa | Iringa Rural | 728 |
| Kilolo | 349 | |
| Ludewa | 564 | |
| Makete | 630 | |
| Mufindi | 520 | |
| Njombe | 2503 | |
| Kagera | Biharamulo | 403 |
| Bukoba Rural | 487 | |
| Bukoba Urban | 88 | |
| Chato | 236 | |
| Karagwe | 771 | |
| Misenyi | 260 | |
| Muleba | 402 | |
| Ngara | 669 | |
| Kigoma | Kasulu | 1047 |
| Kibondo | 874 | |
| Kigoma Rural | 824 | |
| Kigoma Urban | 71 | |
| Kilimanjaro | Hai | 625 |
| Moshi Rural | 1251 | |
| Moshi Urban | 79 | |
| Mwanga | 519 | |
| Rombo | 594 | |
| Same | 877 | |
| Siha | 434 | |
| Lindi | Kilwa | 392 |
| Lindi Rural | 388 | |
| Lindi Urban | 21 | |
| Liwale | 154 | |
| Nachingwea | 300 | |
| Ruangwa | 291 | |
| Manyara | Babati | 511 |
| Hanang | 274 | |
| Kiteto | 193 | |
| Mbulu | 297 | |
| Simanjiro | 308 | |
| Mara | Bunda | 438 |
| Musoma Rural | 396 | |
| Rorya | 210 | |
| Serengeti | 716 | |
| Tarime | 209 | |
| Mbeya | Chunya | 298 |
| Ileje | 231 | |
| Kyela | 859 | |
| Mbarali | 626 | |
| Mbeya Rural | 485 | |
| Mbozi | 1034 | |
| Rungwe | 1106 | |
| Morogoro | Kilombero | 959 |
| Kilosa | 1094 | |
| Morogoro Rural | 521 | |
| Morogoro Urban | 96 | |
| Mvomero | 671 | |
| Ulanga | 665 | |
| Mtwara | Masasi | 528 |
| Mtwara Rural | 423 | |
| Mtwara Urban | 124 | |
| Nanyumbu | 158 | |
| Newala | 231 | |
| Tandahimba | 266 | |
| Mwanza | Geita | 488 |
| Ilemela | 142 | |
| Kwimba | 627 | |
| Magu | 824 | |
| Missungwi | 348 | |
| Nyamagana | 1 | |
| Sengerema | 331 | |
| Ukerewe | 341 | |
| Pwani | Bagamoyo | 997 |
| Kibaha | 269 | |
| Kisarawe | 223 | |
| Mafia | 132 | |
| Mkuranga | 560 | |
| Rufiji | 454 | |
| Rukwa | Mpanda | 679 |
| Nkasi | 428 | |
| Sumbawanga Rural | 521 | |
| Sumbawanga Urban | 180 | |
| Ruvuma | Mbinga | 750 |
| Namtumbo | 694 | |
| Songea Rural | 693 | |
| Songea Urban | 80 | |
| Tunduru | 423 | |
| Shinyanga | Bariadi | 1177 |
| Bukombe | 514 | |
| Kahama | 836 | |
| Kishapu | 399 | |
| Maswa | 809 | |
| Meatu | 468 | |
| Shinyanga Rural | 588 | |
| Shinyanga Urban | 191 | |
| Singida | Iramba | 544 |
| Manyoni | 377 | |
| Singida Rural | 995 | |
| Singida Urban | 177 | |
| Tabora | Igunga | 338 |
| Nzega | 575 | |
| Sikonge | 170 | |
| Tabora Urban | 155 | |
| Urambo | 382 | |
| Uyui | 339 | |
| Tanga | Handeni | 254 |
| Kilindi | 161 | |
| Korogwe | 412 | |
| Lushoto | 694 | |
| Mkinga | 288 | |
| Muheza | 334 | |
| Pangani | 305 | |
| Tanga | 99 |
train_data['public_meeting'].value_counts()
True 51011 False 5055 Name: public_meeting, dtype: int64
ax = pd.DataFrame(train_data['public_meeting'].value_counts()).T.plot(kind='bar')
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
train_data['recorded_by'].value_counts()
GeoData Consultants Ltd 59400 Name: recorded_by, dtype: int64
scheme_mgmt = pd.DataFrame(train_data['scheme_management'].value_counts())
ax = scheme_mgmt.T.plot(kind = 'bar', figsize=(18,9) , colormap = 'Paired', use_index= False)
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
plt.xlabel(' Scheme Management')
Text(0.5, 0, ' Scheme Management')
train_data['scheme_management'].isna().sum()
3877
permit_df = pd.DataFrame(train_data['permit'].value_counts())
ax = permit_df.T.plot(kind = 'bar', use_index = False )
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
plt.xlabel(' Permit')
Text(0.5, 0, ' Permit')
extract_df = pd.DataFrame(train_data['extraction_type'].value_counts())
ax = extract_df.T.plot(kind = 'bar', figsize=(17, 8), use_index=False)
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
plt.xlabel(' Extraction Type')
Text(0.5, 0, ' Extraction Type')
train_data['extraction_type_group'].value_counts()
gravity 26780 nira/tanira 8154 other 6430 submersible 6179 swn 80 3670 mono 2865 india mark ii 2400 afridev 1770 rope pump 451 other handpump 364 other motorpump 122 wind-powered 117 india mark iii 98 Name: extraction_type_group, dtype: int64
train_data['extraction_type'].value_counts()
gravity 26780 nira/tanira 8154 other 6430 submersible 4764 swn 80 3670 mono 2865 india mark ii 2400 afridev 1770 ksb 1415 other - rope pump 451 other - swn 81 229 windmill 117 india mark iii 98 cemo 90 other - play pump 85 walimi 48 climax 32 other - mkulima/shinyanga 2 Name: extraction_type, dtype: int64
train_data['extraction_type_group'].isna().sum()
0
train_data['extraction_type'].isna().sum()
0
train_data['extraction_type_class'].value_counts()
gravity 26780 handpump 16456 other 6430 submersible 6179 motorpump 2987 rope pump 451 wind-powered 117 Name: extraction_type_class, dtype: int64
train_data['extraction_type_class'].isna().sum()
0
mgmt_df = pd.DataFrame(train_data['management'].value_counts())
ax = mgmt_df.T.plot(kind = 'bar', figsize=(16,8), use_index=False)
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
plt.xlabel(' Management')
Text(0.5, 0, ' Management')
mgmt_grp_df = pd.DataFrame(train_data['management_group'].value_counts())
ax = mgmt_grp_df.T.plot(kind = 'bar', figsize=(17, 8), use_index=False)
for p in ax.patches:
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
plt.xlabel(' Management Group')
Text(0.5, 0, ' Management Group')
pay_df = pd.DataFrame(train_data['payment'].value_counts())
ax = pay_df.T.plot(kind = 'bar', figsize=(16, 7), use_index=False)
for p in ax.patches:
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
plt.xlabel(' Payment')
Text(0.5, 0, ' Payment')
train_data['payment_type'].value_counts()
never pay 25348 per bucket 8985 monthly 8300 unknown 8157 on failure 3914 annually 3642 other 1054 Name: payment_type, dtype: int64
train_data['payment'].value_counts()
never pay 25348 pay per bucket 8985 pay monthly 8300 unknown 8157 pay when scheme fails 3914 pay annually 3642 other 1054 Name: payment, dtype: int64
wtr_qlt_df = pd.DataFrame(train_data['water_quality'].value_counts())
ax = wtr_qlt_df.T.plot(kind = 'bar', figsize=(16,8), use_index=False)
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
plt.xlabel(' Water Quality ')
Text(0.5, 0, ' Water Quality ')
qlt_grp_df = pd.DataFrame(train_data['quality_group'].value_counts())
ax = qlt_grp_df.T.plot(kind = 'bar', figsize=(16,8), use_index=False)
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
plt.xlabel(' Quality Group ')
Text(0.5, 0, ' Quality Group ')
train_data['quantity'].value_counts()
enough 33186 insufficient 15129 dry 6246 seasonal 4050 unknown 789 Name: quantity, dtype: int64
train_data['quantity_group'].value_counts()
enough 33186 insufficient 15129 dry 6246 seasonal 4050 unknown 789 Name: quantity_group, dtype: int64
train_data['source'].value_counts()
spring 17021 shallow well 16824 machine dbh 11075 river 9612 rainwater harvesting 2295 hand dtw 874 lake 765 dam 656 other 212 unknown 66 Name: source, dtype: int64
train_data['source_type'].value_counts()
spring 17021 shallow well 16824 borehole 11949 river/lake 10377 rainwater harvesting 2295 dam 656 other 278 Name: source_type, dtype: int64
src_cls_df = pd.DataFrame(train_data['source_class'].value_counts())
ax = src_cls_df.T.plot(kind = 'bar', figsize=(16,8), use_index=False)
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
plt.xlabel(' Source Class ')
Text(0.5, 0, ' Source Class ')
train_data['waterpoint_type'].value_counts()
communal standpipe 28522 hand pump 17488 other 6380 communal standpipe multiple 6103 improved spring 784 cattle trough 116 dam 7 Name: waterpoint_type, dtype: int64
train_data['waterpoint_type_group'].value_counts()
communal standpipe 34625 hand pump 17488 other 6380 improved spring 784 cattle trough 116 dam 7 Name: waterpoint_type_group, dtype: int64
[col for col in train_data.columns if train_data[col].dtypes in ('int64','float64')]
['id', 'amount_tsh', 'gps_height', 'longitude', 'latitude', 'num_private', 'region_code', 'district_code', 'population', 'construction_year']
tsh_df = train_data[train_data['amount_tsh'] != 0]['amount_tsh']
len(tsh_df)
17761
len(train_data[train_data['amount_tsh'] == 0])
41639
tsh_df.hist(bins = 500)
plt.xlim([0, 10000])
(0, 10000)
train_data['gps_height'].isna().sum()
0
train_data['gps_height'].hist(bins = 10)
<matplotlib.axes._subplots.AxesSubplot at 0x1a276d0d30>
train_data['population'].isna().sum()
0
plt.figure(figsize=(17,8))
train_data['population'].hist(bins = 1000)
plt.xlim([0, 3000])
(0, 3000)
train_data['population'].max()
30500
plt.figure(figsize=(17,10))
plt.grid(True)
sns.distplot(train_data['population'] , bins = 500, hist = False)
<matplotlib.axes._subplots.AxesSubplot at 0x1a299f96a0>
[col for col in train_data.columns if train_data[col].dtype == 'datetime64[ns]']
['date_recorded']
train_data['date_recorded'].head()
0 2011-03-14 1 2013-03-06 2 2013-02-25 3 2013-01-28 4 2011-07-13 Name: date_recorded, dtype: datetime64[ns]
year_f = lambda x: x.year
train_data['year_recorded'] = train_data['date_recorded'].apply(year_f)
train_data[['date_recorded','year_recorded']].head()
| date_recorded | year_recorded | |
|---|---|---|
| 0 | 2011-03-14 | 2011 |
| 1 | 2013-03-06 | 2013 |
| 2 | 2013-02-25 | 2013 |
| 3 | 2013-01-28 | 2013 |
| 4 | 2011-07-13 | 2011 |
year_rcrd = train_data['year_recorded'].astype('str')
year_rcrd = year_rcrd.value_counts()
ax = year_rcrd.plot(kind = 'bar' , figsize=(16, 5))
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
plt.xlabel(' Year Recorded ')
plt.grid(True)
train_data['month_recorded'] = train_data['date_recorded'].apply(lambda x: x.month)
train_data[['date_recorded','month_recorded']].head()
| date_recorded | month_recorded | |
|---|---|---|
| 0 | 2011-03-14 | 3 |
| 1 | 2013-03-06 | 3 |
| 2 | 2013-02-25 | 2 |
| 3 | 2013-01-28 | 1 |
| 4 | 2011-07-13 | 7 |
month_rcrd = train_data['month_recorded'].astype('str')
ax = month_rcrd.value_counts().plot(kind = 'bar', figsize=(17, 8))
for p in ax.patches:
ax.annotate(str(p.get_height()), (p.get_x() * 1.007, p.get_height() * 1.007))
plt.xlabel(' Month Recorded ')
plt.grid(True)
train_data['construction_year'].isna().sum()
0
train_data[['date_recorded','construction_year','year_recorded']].head()
| date_recorded | construction_year | year_recorded | |
|---|---|---|---|
| 0 | 2011-03-14 | 1999 | 2011 |
| 1 | 2013-03-06 | 2010 | 2013 |
| 2 | 2013-02-25 | 2009 | 2013 |
| 3 | 2013-01-28 | 1986 | 2013 |
| 4 | 2011-07-13 | 0 | 2011 |
train_data['years_elapsed'] = train_data['year_recorded'] - train_data['construction_year']
train_data[['date_recorded','construction_year','year_recorded','years_elapsed']].head()
| date_recorded | construction_year | year_recorded | years_elapsed | |
|---|---|---|---|---|
| 0 | 2011-03-14 | 1999 | 2011 | 12 |
| 1 | 2013-03-06 | 2010 | 2013 | 3 |
| 2 | 2013-02-25 | 2009 | 2013 | 4 |
| 3 | 2013-01-28 | 1986 | 2013 | 27 |
| 4 | 2011-07-13 | 0 | 2011 | 2011 |
len(train_data[train_data['construction_year'] == 0])
20709
from scipy.stats import mode
mode(train_data[train_data['construction_year'] != 0]['construction_year'])
ModeResult(mode=array([2010], dtype=int64), count=array([2645]))
train_data['const_yr_impt_2010'] = np.where(train_data['construction_year'] == 0, 2010, train_data['construction_year'])
train_data['const_yr_impt_2010'].head()
0 1999 1 2010 2 2009 3 1986 4 2010 Name: const_yr_impt_2010, dtype: int64
train_data['years_elapsed'] = train_data['year_recorded'] - train_data['const_yr_impt_2010']
train_data[['date_recorded','construction_year','const_yr_impt_2010','year_recorded','years_elapsed']].head()
| date_recorded | construction_year | const_yr_impt_2010 | year_recorded | years_elapsed | |
|---|---|---|---|---|---|
| 0 | 2011-03-14 | 1999 | 1999 | 2011 | 12 |
| 1 | 2013-03-06 | 2010 | 2010 | 2013 | 3 |
| 2 | 2013-02-25 | 2009 | 2009 | 2013 | 4 |
| 3 | 2013-01-28 | 1986 | 1986 | 2013 | 27 |
| 4 | 2011-07-13 | 0 | 2010 | 2011 | 1 |
train_data.head(10)
| id | amount_tsh | date_recorded | funder | gps_height | installer | longitude | latitude | wpt_name | num_private | basin | subvillage | region | region_code | district_code | lga | ward | population | public_meeting | recorded_by | scheme_management | scheme_name | permit | construction_year | extraction_type | extraction_type_group | extraction_type_class | management | management_group | payment | payment_type | water_quality | quality_group | quantity | quantity_group | source | source_type | source_class | waterpoint_type | waterpoint_type_group | year_recorded | month_recorded | years_elapsed | const_yr_impt_2010 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 69572 | 6000.0 | 2011-03-14 | Roman | 1390 | Roman | 34.938093 | -9.856322 | none | 0 | Lake Nyasa | Mnyusi B | Iringa | 11 | 5 | Ludewa | Mundindi | 109 | True | GeoData Consultants Ltd | VWC | Roman | False | 1999 | gravity | gravity | gravity | vwc | user-group | pay annually | annually | soft | good | enough | enough | spring | spring | groundwater | communal standpipe | communal standpipe | 2011 | 3 | 12 | 1999 |
| 1 | 8776 | 0.0 | 2013-03-06 | Grumeti | 1399 | GRUMETI | 34.698766 | -2.147466 | Zahanati | 0 | Lake Victoria | Nyamara | Mara | 20 | 2 | Serengeti | Natta | 280 | NaN | GeoData Consultants Ltd | Other | NaN | True | 2010 | gravity | gravity | gravity | wug | user-group | never pay | never pay | soft | good | insufficient | insufficient | rainwater harvesting | rainwater harvesting | surface | communal standpipe | communal standpipe | 2013 | 3 | 3 | 2010 |
| 2 | 34310 | 25.0 | 2013-02-25 | Lottery Club | 686 | World vision | 37.460664 | -3.821329 | Kwa Mahundi | 0 | Pangani | Majengo | Manyara | 21 | 4 | Simanjiro | Ngorika | 250 | True | GeoData Consultants Ltd | VWC | Nyumba ya mungu pipe scheme | True | 2009 | gravity | gravity | gravity | vwc | user-group | pay per bucket | per bucket | soft | good | enough | enough | dam | dam | surface | communal standpipe multiple | communal standpipe | 2013 | 2 | 4 | 2009 |
| 3 | 67743 | 0.0 | 2013-01-28 | Unicef | 263 | UNICEF | 38.486161 | -11.155298 | Zahanati Ya Nanyumbu | 0 | Ruvuma / Southern Coast | Mahakamani | Mtwara | 90 | 63 | Nanyumbu | Nanyumbu | 58 | True | GeoData Consultants Ltd | VWC | NaN | True | 1986 | submersible | submersible | submersible | vwc | user-group | never pay | never pay | soft | good | dry | dry | machine dbh | borehole | groundwater | communal standpipe multiple | communal standpipe | 2013 | 1 | 27 | 1986 |
| 4 | 19728 | 0.0 | 2011-07-13 | Action In A | 0 | Artisan | 31.130847 | -1.825359 | Shuleni | 0 | Lake Victoria | Kyanyamisa | Kagera | 18 | 1 | Karagwe | Nyakasimbi | 0 | True | GeoData Consultants Ltd | NaN | NaN | True | 0 | gravity | gravity | gravity | other | other | never pay | never pay | soft | good | seasonal | seasonal | rainwater harvesting | rainwater harvesting | surface | communal standpipe | communal standpipe | 2011 | 7 | 1 | 2010 |
| 5 | 9944 | 20.0 | 2011-03-13 | Mkinga Distric Coun | 0 | DWE | 39.172796 | -4.765587 | Tajiri | 0 | Pangani | Moa/Mwereme | Tanga | 4 | 8 | Mkinga | Moa | 1 | True | GeoData Consultants Ltd | VWC | Zingibali | True | 2009 | submersible | submersible | submersible | vwc | user-group | pay per bucket | per bucket | salty | salty | enough | enough | other | other | unknown | communal standpipe multiple | communal standpipe | 2011 | 3 | 2 | 2009 |
| 6 | 19816 | 0.0 | 2012-10-01 | Dwsp | 0 | DWSP | 33.362410 | -3.766365 | Kwa Ngomho | 0 | Internal | Ishinabulandi | Shinyanga | 17 | 3 | Shinyanga Rural | Samuye | 0 | True | GeoData Consultants Ltd | VWC | NaN | True | 0 | swn 80 | swn 80 | handpump | vwc | user-group | never pay | never pay | soft | good | enough | enough | machine dbh | borehole | groundwater | hand pump | hand pump | 2012 | 10 | 2 | 2010 |
| 7 | 54551 | 0.0 | 2012-10-09 | Rwssp | 0 | DWE | 32.620617 | -4.226198 | Tushirikiane | 0 | Lake Tanganyika | Nyawishi Center | Shinyanga | 17 | 3 | Kahama | Chambo | 0 | True | GeoData Consultants Ltd | NaN | NaN | True | 0 | nira/tanira | nira/tanira | handpump | wug | user-group | unknown | unknown | milky | milky | enough | enough | shallow well | shallow well | groundwater | hand pump | hand pump | 2012 | 10 | 2 | 2010 |
| 8 | 53934 | 0.0 | 2012-11-03 | Wateraid | 0 | Water Aid | 32.711100 | -5.146712 | Kwa Ramadhan Musa | 0 | Lake Tanganyika | Imalauduki | Tabora | 14 | 6 | Tabora Urban | Itetemia | 0 | True | GeoData Consultants Ltd | VWC | NaN | True | 0 | india mark ii | india mark ii | handpump | vwc | user-group | never pay | never pay | salty | salty | seasonal | seasonal | machine dbh | borehole | groundwater | hand pump | hand pump | 2012 | 11 | 2 | 2010 |
| 9 | 46144 | 0.0 | 2011-08-03 | Isingiro Ho | 0 | Artisan | 30.626991 | -1.257051 | Kwapeto | 0 | Lake Victoria | Mkonomre | Kagera | 18 | 1 | Karagwe | Kaisho | 0 | True | GeoData Consultants Ltd | NaN | NaN | True | 0 | nira/tanira | nira/tanira | handpump | vwc | user-group | never pay | never pay | soft | good | enough | enough | shallow well | shallow well | groundwater | hand pump | hand pump | 2011 | 8 | 1 | 2010 |
#### 34.93809275 -9.85632177
#### Lake Nysa - 11.6701° S, 34.6857° E
train_data['basin'].value_counts()
Lake Victoria 10248 Pangani 8940 Rufiji 7976 Internal 7785 Lake Tanganyika 6432 Wami / Ruvu 5987 Lake Nyasa 5085 Ruvuma / Southern Coast 4493 Lake Rukwa 2454 Name: basin, dtype: int64
# function to convert degree, minutes , seconds to just to degree .
def conv_degree(lat):
lat = lat.replace(" ","")
deg, minutes, seconds, direction = re.split('[°\'"]', lat)
return (float(deg) + float(minutes)/60 + float(seconds)/(60*60)) * (-1 if direction in ['W', 'S'] else 1)
# 'Lake Victoria' = Latitude: -1° 00' 0.00" S Longitude: 33° 00' 0.00" E
lat_1 = conv_degree("""-1° 00' 0.00" S""")
log_1 = conv_degree("""33° 00' 0.00" E""")
# 'Pangani' = Latitude: -5° 25' 30.94" S Longitude: 38° 58' 29.03" E
lat_2 = conv_degree("""-5° 25' 30.94" S""")
log_2 = conv_degree("""38° 58' 29.03" E""")
# 'Rufiji' = Latitude: -8° 00' 0.00" S Longitude: 39° 19' 60.00" E
lat_3 = conv_degree("""-8° 00' 0.00" S""")
log_3 = conv_degree("""39° 19' 60.00" E""")
# 'Internal'(same as ruvu) = Latitude: -6° 48' 17.99" S Longitude: 38° 41' 59.99" E
lat_4 = conv_degree("""-6° 48' 17.99" S""")
log_4 = conv_degree("""38° 41' 59.99" E""")
# 'Lake Tanganyika' = 6.2556° S, 29.5108° E
lat_5 = conv_degree("""6.2556° 0' 00" S""")
log_5 = conv_degree("""29.5108° 0' 00" E""")
# 'Wami / Ruvu' - Latitude: -6° 07' 60.00" S Longitude: 38° 48' 59.99" E
lat_6 = conv_degree("""-6° 07' 60.00" S""")
log_6 = conv_degree("""38° 48' 59.99" E""")
# 'Lake Nyasa' = 11.6701° S, 34.6857° E
lat_7 = conv_degree("""11.6701° 0' 00" S""")
log_7 = conv_degree("""34.6857° 0' 00" E""")
# 'Ruvuma / Southern Coast' = 10.6879° S, 36.2631° E
lat_8 = conv_degree("""10.6879° 0' 00" S""")
log_8 = conv_degree("""36.2631° 0' 00" E""")
#'Lake Rukwa' = 8.0167° S, 32.2654° E
lat_9 = conv_degree("""8.0167° 0' 00" S""")
log_9 = conv_degree("""32.2654° 0' 00" E""")
def get_basin_lat_long(basin , lat_or_long):
if basin == "Lake Victoria":
latitude = lat_1
longitude = log_1
elif basin == "Pangani":
latitude = lat_2
longitude = log_2
elif basin == "Rufiji":
latitude = lat_3
longitude = log_3
elif basin == "Internal":
latitude = lat_4
longitude = log_4
elif basin == "Lake Tanganyika":
latitude = lat_5
longitude = log_5
elif basin == "Wami / Ruvu":
latitude = lat_6
longitude = log_6
elif basin == "Lake Nyasa":
latitude = lat_7
longitude = log_7
elif basin == "Ruvuma / Southern Coast":
latitude = lat_8
longitude = log_8
elif basin == "Lake Rukwa":
latitude = lat_9
longitude = log_9
if lat_or_long.lower() == 'latitude':
return latitude
elif lat_or_long.lower() == 'longitude':
return longitude
get_basin_lat_long("Lake Tanganyika", 'latitude')
-6.2556
train_data['basin_latitude'] = train_data['basin'].apply(get_basin_lat_long, lat_or_long = 'latitude')
train_data['basin_longitude'] = train_data['basin'].apply(get_basin_lat_long, lat_or_long = 'longitude')
# function to calculate the distance of pumps from the respective basins in KMs
def dist_from_basin(basin_lat, basin_long, pump_lat, pump_long):
coords_1 = (basin_lat.astype('float64'), basin_long.astype('float64'))
coords_2 = (pump_lat.astype('float64'), pump_long.astype('float64'))
return geopy.distance.vincenty(coords_1, coords_2).km
dist_list = []
for index, row in train_data[['basin_latitude','basin_longitude','latitude','longitude']].iterrows():
dist = dist_from_basin(row['basin_latitude'], row['basin_longitude'], row['latitude'], row['longitude'])
dist_list.append(dist)
/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: DeprecationWarning: Vincenty is deprecated and is going to be removed in geopy 2.0. Use `geopy.distance.geodesic` (or the default `geopy.distance.distance`) instead, which is more accurate and always converges.
len(dist_list)
59400
train_data['basin_pump_dist'] = pd.DataFrame({'basin_pump_dist':dist_list})
train_data[['basin_latitude','basin_longitude','latitude','longitude','basin_pump_dist']].head()
| basin_latitude | basin_longitude | latitude | longitude | basin_pump_dist | |
|---|---|---|---|---|---|
| 0 | -11.670100 | 34.685700 | -9.856322 | 34.938093 | 202.517704 |
| 1 | 1.000000 | 33.000000 | -2.147466 | 34.698766 | 396.072413 |
| 2 | 4.574739 | 38.974731 | -3.821329 | 37.460664 | 943.553742 |
| 3 | -10.687900 | 36.263100 | -11.155298 | 38.486161 | 248.454311 |
| 4 | 1.000000 | 33.000000 | -1.825359 | 31.130847 | 375.346864 |
train_data['basin_pump_dist'].isna().sum()
0
num_features = [col for col in train_data.columns if train_data[col].dtype in ('int64', 'float64')]
sns.pairplot(train_data[num_features])
<seaborn.axisgrid.PairGrid at 0x1a2609d748>
plt.figure(figsize=(17, 10))
sns.heatmap(train_data[num_features].corr(), annot=True)
<matplotlib.axes._subplots.AxesSubplot at 0x1a32b2dba8>
redundant_cols = ['id','longitude', 'latitude', 'num_private','region_code','construction_year','year_recorded','month_recorded','basin_latitude','basin_longitude']
train_data[num_features].drop(redundant_cols, axis=1).head()
| amount_tsh | gps_height | district_code | population | years_elapsed | const_yr_impt_2010 | basin_pump_dist | |
|---|---|---|---|---|---|---|---|
| 0 | 6000.0 | 1390 | 5 | 109 | 12 | 1999 | 202.517704 |
| 1 | 0.0 | 1399 | 2 | 280 | 3 | 2010 | 396.072413 |
| 2 | 25.0 | 686 | 4 | 250 | 4 | 2009 | 943.553742 |
| 3 | 0.0 | 263 | 63 | 58 | 27 | 1986 | 248.454311 |
| 4 | 0.0 | 0 | 1 | 0 | 1 | 2010 | 375.346864 |
sns.pairplot(train_data[num_features].drop(redundant_cols, axis=1))
<seaborn.axisgrid.PairGrid at 0x1a4349d978>
plt.figure(figsize=(17,10))
sns.heatmap((train_data[num_features].drop(redundant_cols, axis=1)).corr(), annot=True)
<matplotlib.axes._subplots.AxesSubplot at 0x1a496e0d30>
train_data_1 = train_data.drop(redundant_cols,axis=1)
train_data_1.shape
(59400, 37)
train_data_1.isna().sum()
amount_tsh 0 date_recorded 0 funder 3635 gps_height 0 installer 3655 wpt_name 0 basin 0 subvillage 371 region 0 district_code 0 lga 0 ward 0 population 0 public_meeting 3334 recorded_by 0 scheme_management 3877 scheme_name 28166 permit 3056 extraction_type 0 extraction_type_group 0 extraction_type_class 0 management 0 management_group 0 payment 0 payment_type 0 water_quality 0 quality_group 0 quantity 0 quantity_group 0 source 0 source_type 0 source_class 0 waterpoint_type 0 waterpoint_type_group 0 years_elapsed 0 const_yr_impt_2010 0 basin_pump_dist 0 dtype: int64
del(train_data)
train_data_1['funder'].fillna('funder_missing', inplace=True)
train_data_1['installer'].fillna('installer_missing', inplace=True)
train_data_1.drop('subvillage', axis=1, inplace=True)
train_data_1['public_meeting'].fillna('miss_p_mtng', inplace=True)
train_data_1['scheme_management'].fillna('miss_sch_mngt', inplace=True)
train_data_1.drop('scheme_name', axis=1, inplace=True)
train_data_1['permit'].fillna('miss_permit', inplace=True)
##### Now again check if there is any missing or null values
train_data_1[train_data_1.isna().any(axis=1)]
| amount_tsh | date_recorded | funder | gps_height | installer | wpt_name | basin | region | district_code | lga | ward | population | public_meeting | recorded_by | scheme_management | permit | extraction_type | extraction_type_group | extraction_type_class | management | management_group | payment | payment_type | water_quality | quality_group | quantity | quantity_group | source | source_type | source_class | waterpoint_type | waterpoint_type_group | years_elapsed | const_yr_impt_2010 | basin_pump_dist |
|---|
train_data_1.shape
(59400, 35)
train_data_1.drop(['date_recorded','const_yr_impt_2010'], inplace=True, axis=1)
train_data_1.columns
Index(['amount_tsh', 'funder', 'gps_height', 'installer', 'wpt_name', 'basin',
'region', 'district_code', 'lga', 'ward', 'population',
'public_meeting', 'recorded_by', 'scheme_management', 'permit',
'extraction_type', 'extraction_type_group', 'extraction_type_class',
'management', 'management_group', 'payment', 'payment_type',
'water_quality', 'quality_group', 'quantity', 'quantity_group',
'source', 'source_type', 'source_class', 'waterpoint_type',
'waterpoint_type_group', 'years_elapsed', 'basin_pump_dist'],
dtype='object')
redundant_cols_new = ['district_code',
'waterpoint_type_group',
'source_type',
'source_class',
'quantity_group',
'quality_group',
'payment_type',
'management_group',
'extraction_type_group',
'extraction_type_class',
'recorded_by',
'lga',
'ward',
'basin',
'wpt_name']
train_data_1.drop(redundant_cols_new, axis=1, inplace=True)
train_data_1.columns
Index(['amount_tsh', 'funder', 'gps_height', 'installer', 'region',
'population', 'public_meeting', 'scheme_management', 'permit',
'extraction_type', 'management', 'payment', 'water_quality', 'quantity',
'source', 'waterpoint_type', 'years_elapsed', 'basin_pump_dist'],
dtype='object')
train_data_1.shape
(59400, 18)
train_data_1.head()
| amount_tsh | funder | gps_height | installer | region | population | public_meeting | scheme_management | permit | extraction_type | management | payment | water_quality | quantity | source | waterpoint_type | years_elapsed | basin_pump_dist | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6000.0 | Roman | 1390 | Roman | Iringa | 109 | True | VWC | False | gravity | vwc | pay annually | soft | enough | spring | communal standpipe | 12 | 202.517704 |
| 1 | 0.0 | Grumeti | 1399 | GRUMETI | Mara | 280 | miss_p_mtng | Other | True | gravity | wug | never pay | soft | insufficient | rainwater harvesting | communal standpipe | 3 | 396.072413 |
| 2 | 25.0 | Lottery Club | 686 | World vision | Manyara | 250 | True | VWC | True | gravity | vwc | pay per bucket | soft | enough | dam | communal standpipe multiple | 4 | 943.553742 |
| 3 | 0.0 | Unicef | 263 | UNICEF | Mtwara | 58 | True | VWC | True | submersible | vwc | never pay | soft | dry | machine dbh | communal standpipe multiple | 27 | 248.454311 |
| 4 | 0.0 | Action In A | 0 | Artisan | Kagera | 0 | True | miss_sch_mngt | True | gravity | other | never pay | soft | seasonal | rainwater harvesting | communal standpipe | 1 | 375.346864 |
# get the left over numerical columns for scaling
num_col_sc = [col for col in train_data_1.columns if train_data_1[col].dtype in('int64','float64')]
num_col_sc
['amount_tsh', 'gps_height', 'population', 'years_elapsed', 'basin_pump_dist']
train_data_1 = pd.get_dummies(train_data_1)
train_data_1.shape
(59400, 4156)
train_data_1.head()
| amount_tsh | gps_height | population | years_elapsed | basin_pump_dist | funder_0 | funder_A/co Germany | funder_Aar | funder_Abas Ka | funder_Abasia | funder_Abc-ihushi Development Cent | funder_Abd | funder_Abdala | funder_Abddwe | funder_Abdul | funder_Abood | funder_Abs | funder_Aco/germany | funder_Acord | funder_Acord Ngo | funder_Acra | funder_Act | funder_Act Mara | funder_Action Aid | funder_Action Contre La Faim | funder_Action In A | funder_Adap | funder_Adb | funder_Adf | funder_Adp | funder_Adp Bungu | funder_Adp Mombo | funder_Adp/w | funder_Adra | funder_Af | funder_Afdp | funder_Afric | funder_Africa | funder_Africa 2000 Network/undp | funder_Africa Amini Alama | funder_Africa Project Ev Germany | funder_African | funder_African 2000 Network | funder_African Barrick Gold | funder_African Development Bank | funder_African Development Foundation | funder_African Muslim Agency | funder_African Realief Committe Of Ku | funder_African Reflections Foundation | funder_African Relie | funder_Africaone Ltd | funder_Africare | funder_Afriican Reli | funder_Afroz Ismail | funder_Afya Department Lindi Rural | funder_Agape Churc | funder_Agt Church | funder_Ahmadia | funder_Ai | funder_Aic | funder_Aic Church | funder_Aic Kij | funder_Aict | funder_Aimgold | funder_Aixos | funder_Alia | funder_Ambwene Mwaikek | funder_Amref | funder_Amrefe | funder_Anglican Church | funder_Angrikana | funder_Anjuman E Seifee | funder_Answeer Muslim Grou | funder_Apm | funder_Apm[africa Precious Metals Lt | funder_Aqua Blues Angels | funder_Arab Community | funder_Arabi | funder_Arabs Community | funder_Ardhi Instute | funder_Area | funder_Artisan | funder_Asb | funder_Asdp | funder_Asgerali N Bharwan | funder_Auwasa | funder_Awf | funder_B.A.P | funder_Ba As | funder_Babtest | funder_Babtist | funder_Bahewasa | funder_Bahresa | funder_Bakari Chimkube | funder_Bakwata | funder_Ballo | funder_Balo | funder_Balyehe | funder_Banca Reale | funder_Bank | funder_Bao | funder_Baptist Church | funder_Baric | funder_Bathlomew Vicent | funder_Batist Church | funder_Belgian Government | funder_Belgij | funder_Bened | funder_Benguka | funder_Bffs | funder_Bfwd | funder_Bgm | funder_Bgss | funder_Bgssws | funder_Bhws | funder_Bilila | funder_Bingo Foundation | funder_Bingo Foundation Germany | funder_Bio Fuel Company | funder_Biore | funder_Birage | funder_Bkhws | funder_Boazi | funder_Boazi /o | funder_Bobby | funder_Bokera W | funder_Boma Saving | funder_Bong-kug Ohh/choonlza Lee | funder_Bonite Bottles Ltd | funder_Br | funder_Bra | funder_Brad | funder_Brdp | funder_Bread For The Wor | funder_Bread Of The Worl | funder_Bridge North | funder_British Colonial Government | funder_British Tanza | funder_Brown | funder_Bruder | funder_Bs | funder_Bsf | funder_Bukumbi | funder_Bukwang Church Saint | funder_Bukwang Church Saints | funder_Buluga Subvillage Community | funder_Bulyahunlu Gold Mine | funder_Bumabu | funder_Buptist | funder_Busoga Trust | funder_C | funder_Cafod | funder_Caltas | funder_Caltas Tanzania | funder_Caltaz Kahama | funder_Caltus | funder_Calvary Connect | funder_Camartec | funder_Camavita | funder_Canada | funder_Canada Aid | funder_Care Int | funder_Care International | funder_Care/cipro | funder_Care/dwe | funder_Caritas | funder_Carmatech | funder_Cartas Tanzania | funder_Cast | funder_Cathoric | funder_Cbhi | funder_Cc Motor Day 2010 | funder_Ccp | funder_Ccpk | funder_Ccps | funder_Cct | funder_Cdcg | funder_Cdft | funder_Cdg | funder_Cdtf | funder_Cdtfdistrict Council | funder_Cefa | funder_Cefa-njombe | funder_Cefa/rcchurch | funder_Ces (gmbh) | funder_Ces(gmbh) | funder_Cg | funder_Cg/rc | funder_Cgc | funder_Cgi | funder_Ch | funder_Chacha | funder_Chacha Issame | funder_Chai Wazir | funder_Chama Cha Ushirika | funder_Chamavita | funder_Chani | funder_Charlotte Well | funder_Cheni | funder_China Government | funder_Chmavita | funder_Chongolo | funder_Christan Outrich | funder_Christian Outrich | funder_Chuo | funder_Churc | funder_Church | funder_Church Of Disciples | funder_Cida | funder_Cip | funder_Cipro | funder_Cipro/care | funder_Cipro/care/tcrs | funder_Cipro/government | funder_Clause | funder_Cmcr | funder_Cmsr | funder_Co | funder_Cobashec | funder_Cocen | funder_Cocern | funder_Cocu | funder_College | funder_Colonial Government | funder_Commu | funder_Community | funder_Community Bank | funder_Compa | funder_Company | funder_Compasion International | funder_Comune Di Roma | funder_Comunedi Roma | funder_Comunity Construction Fund | funder_Conce | funder_Concen | funder_Concern | funder_Concern /govern | funder_Concern World Wide | funder_Concern/governm | funder_Cope | funder_Costantine Herman | funder_Council | funder_Cpar | funder_Cper | funder_Cpps | funder_Cpps Mission | funder_Cpro | funder_Craelius | funder_Cristan Outrich | funder_Crs | funder_Csf | funder_Cspd | funder_Cvs Miss | funder_D | funder_D Ct | funder_Da Unoperaio Siciliano | funder_Dacp | funder_Dadid | funder_Dadis | funder_Dadp | funder_Dads | funder_Dae Yeol And Chae Lynn | funder_Dagida | funder_Daida | funder_Dak | funder_Daldo | funder_Danida | funder_Danida /government | funder_Dar Al Ber | funder_Dar Es Salaam Round Table | funder_Dasiip | funder_Dasip | funder_Dasp | funder_Dasp Ltd | funder_Dassip | funder_Dawasa | funder_Dawasco | funder_Dbfpe | funder_Dbsp | funder_Dbspe | funder_Dct | funder_Ddca | funder_Ddp | funder_De | funder_Ded | funder_Ded Kilo | funder_Ded/rwssp | funder_Ded_rwsp | funder_Denat | funder_Denish | funder_Deogratius Kasima | funder_Desk And Chair Foundation | funder_Devon Aid Korogwe | funder_Dfid | funder_Dgv | funder_Dh | funder_Dhinu | funder_Dhv | funder_Dhv Moro | funder_Dhv/gove | funder_Dhv\norp | funder_Dhv\swis | funder_Dimon | funder_Dina | funder_Dioce | funder_Diocese Of Geita | funder_Diocese Of Mount Kilimanjaro | funder_District Council | funder_District Medical | funder_District Rural Project | funder_Diwani | funder_Dmd | funder_Dmdd | funder_Dmdd/solider | funder_Dmk | funder_Dmk Anglican | funder_Dmmd | funder_Dmo | funder_Do | funder_Doctor Mwambi | funder_Doddea | funder_Dokta Mwandulam | funder_Dom | funder_Domestic Rural Development Pr | funder_Domestic Rural Development Pro | funder_Domestic Water Supply Project | funder_Dominiki Simwen | funder_Doner And Com | funder_Doner And Ded | funder_Donor | funder_Dqnida | funder_Drdp | funder_Drdp Ngo | funder_Drv Na Idara | funder_Drwssp | funder_Dsdp | funder_Dsp | funder_Duka | funder_Duwas | funder_Dv | funder_Dw | funder_Dwarf | funder_Dwe | funder_Dwe And Veo | funder_Dwe/anglican Church | funder_Dwe/bamboo Projec | funder_Dwe/norad | funder_Dwe/rudep | funder_Dwe/ubalozi Wa Marekani | funder_Dwsdp | funder_Dwsp | funder_Dwssp | funder_Dwst | funder_Dwt | funder_Ea | funder_Eastmeru Medium School | funder_Eater | funder_Ebaha | funder_Eco Lodge | funder_Education Funds | funder_Efg | funder_Egypt | funder_Egypt Government | funder_Egypt Technical Co Operation | funder_El | funder_Elca | funder_Elct | funder_Embasy Of Japan In Tanzania | funder_Engin | funder_Engineers Without Border | funder_Eno | funder_Enyuati | funder_Enyueti | funder_Ereto | funder_Ermua | funder_Erre Kappa | funder_Esawa | funder_Ester Ndege | funder_Eu | funder_Eu/acra | funder_Eung Am Methodist Church | funder_Eung-am Methodist Church | funder_European Union | funder_F | funder_Fabia | funder_Fao | funder_Farm Africa | funder_Farm-africa | funder_Fathe | funder_Father Bonifasi | funder_Father W | funder_Fdc | funder_Fida | funder_Filo | funder_Fin Water | funder_Fini Water | funder_Finida German Tanzania Govt | funder_Finidagermantanzania Govt | funder_Finland | funder_Finland Government | funder_Finn Water | funder_Finw | funder_Finwater | funder_Fiwater | funder_Floresta | funder_Folac | funder_Foreigne | funder_Fosecu | funder_Fpct | funder_Fpct Church | funder_Fpct Mulala | funder_Fptc - Pent | funder_Franc | funder_France | funder_Frankfurt | funder_Fredked Conservation | funder_Free Pentecoste Church Of Tanz | funder_Fresh Water Plc England | funder_Friedkin Conservation Fund | funder_Friend From Un | funder_Friends Of Kibara Foundation | funder_Friends Of Ulambo And Mwanhala | funder_Full Gospel Church | funder_Fw | funder_G.D&i.D | funder_Ga | funder_Gachuma Ginery | funder_Gaica | funder_Gain | funder_Game Division | funder_Game Fronti | funder_Gdp | funder_Geita Goldmain | funder_Gen | funder_Geochaina | funder_Gerald Tuseko Gro | funder_German Missionary | funder_Germany | funder_Germany Cristians | funder_Germany Misionary | funder_Germany Missionary | funder_Germany Republi | funder_Gesawa | funder_Getdsc00 | funder_Getekwe | funder_Gg | funder_Ggm | funder_Gil Cafe'church' | funder_Giovan Disinistra Per Salve | funder_Giz | funder_Global Fund | funder_Go | funder_Godii | funder_Goldmain | funder_Goldwill Foundation | funder_Government | funder_Government /sda | funder_Government /tassaf | funder_Government /world Vision | funder_Government And Community | funder_Government Of Misri | funder_Government Of Tanzania | funder_Government/ Community | funder_Government/ World Bank | funder_Government/school | funder_Government/tassaf | funder_Government/tcrs | funder_Gra Na Halmashauri | funder_Grail Mission Kiseki Bar | funder_Grazie Franco Lucchini | funder_Grazie Grouppo Padre Fiorentin | funder_Greec | funder_Greinaker | funder_Greineker | funder_Grumeti | funder_Gt | funder_Gtz | funder_Gurdians | funder_Gwitembe | funder_H | funder_H/w | funder_H4ccp | funder_Haam | funder_Haidomu Lutheran Church | funder_Halimashau | funder_Halimashauli | funder_Halmashauli | funder_Halmashaur | funder_Halmashauri | funder_Halmashauri Wil | funder_Halmashauri Ya Manispa Tabora | funder_Halmashauri Ya Wilaya | funder_Halmashauri Ya Wilaya Sikonge | funder_Ham | funder_Hamref | funder_Handeni Trunk Main( | funder_Handeni Trunk Maini | funder_Hans | funder_Hapa | funder_Hapa Singida | funder_Happy Watoto Foundation | funder_Haruna Mpog | funder_Hasawa | funder_Hashi | funder_Hasnan Murig (mbunge) | funder_Hasnein Muij Mbunge | funder_Hasnein Murij | funder_Hassan Gulam | funder_Haydom Lutheran Hospital | funder_Hdv | funder_He | funder_Healt | funder_Health Ministry | funder_Hearts Helping Hands.Inc. | funder_Henure Dema | funder_Heri Mission | funder_Hery | funder_Hesaw | funder_Hesawa | funder_Hesawa And Concern World Wide | funder_Hesawwa | funder_Hesawz | funder_Hesawza | funder_Hesswa | funder_Hewasa | funder_Hewawa | funder_Hez | funder_Hhesawa | funder_Hiap | funder_Hifab | funder_Hilfe Fur Brunder | funder_Hindu | funder_Holand | funder_Holili Water Supply | funder_Holla | funder_Holland | funder_Hongoli | funder_Hortanzia | funder_Hospital | funder_Hotels And Lodge Tanzania | funder_Hotels And Loggs Tz Ltd | funder_Hpa | funder_Hsw | funder_Htm | funder_Huches | funder_Hw/rc | funder_Hydom Luthelani | funder_I Wash | funder_I.E.C | funder_Iado | funder_Icap | funder_Icdp | funder_Icf | funder_Ics | funder_Idara Ya Maji | funder_Idc | funder_Idea | funder_Idf | funder_Idydc | funder_If | funder_Ifad | funder_Ifakara | funder_Igolola Community | funder_Ikela Wa | funder_Ikeuchi Towels Japan | funder_Il | funder_Ilaramataki | funder_Ilct | funder_Ilkeri Village | funder_Ilo | funder_Ilo/undp | funder_Ilwilo Community | funder_Imf | funder_In | funder_In Memoria Di Albeto | funder_Incerto | funder_Inkinda | funder_Insititutiona | funder_Institution | funder_Institutional | funder_Insututional | funder_Internal Drainage Basin | funder_International Aid Services | funder_Investor | funder_Iom | funder_Ir | funder_Iran Gover | funder_Irc | funder_Irevea Sister | funder_Irevea Sister Water | funder_Irish Ai | funder_Irish Government | funder_Is | funder_Isf | funder_Isf / Tasaff | funder_Isf/government | funder_Isf/gvt | funder_Isf/tacare | funder_Isingiro Ho | funder_Islam | funder_Islamic | funder_Islamic Agency Tanzania | funder_Islamic Community | funder_Islamic Found | funder_Islamic Society | funder_Isnashia And | funder_Issa Mohamedi Tumwanga | funder_Italian | funder_Italy | funder_Italy Government | funder_Iucn | funder_Jacobin | funder_Jafary Mbaga | funder_Jaica | funder_Jamal | funder_Jamal Abdallah | funder_Japan | funder_Japan Food Aid Counter Part | funder_Japan Aid | funder_Japan Embassy | funder_Japan Food | funder_Japan Food Aid | funder_Japan Government | funder_Jbg | funder_Jeica | funder_Jeshi La Wokovu | funder_Jeshi La Wokovu [cida] | funder_Jeshi Lawokovu | funder_Jgb | funder_Jica | funder_Jika | funder_Jimbo Fund | funder_Jimmy | funder_Jipa | funder_John Fund | funder_John Gileth | funder_John Skwese | funder_Ju | funder_Ju-sarang Church' And Bugango | funder_Judge Mchome | funder_Juhibu | funder_Juma | funder_Jumaa | funder_Jumanne | funder_Jumanne Siabo | funder_Justine Marwa | funder_Jwtz | funder_K | funder_Ka | funder_Kaaya | funder_Kadip | funder_Kadp | funder_Kadres Ngo | funder_Kaemp | funder_Kagera | funder_Kagera Mine | funder_Kagunguli Secondary | funder_Kahema | funder_Kajima | funder_Kalebejo Parish | funder_Kalitasi | funder_Kalitesi | funder_Kalta | funder_Kamama | funder_Kamata Project | funder_Kambi Migoko | funder_Kanamama | funder_Kando | funder_Kanis | funder_Kanisa | funder_Kanisa Katoliki | funder_Kanisa Katoliki Lolovoni | funder_Kanisa La Menonite | funder_Kanisa La Mitume | funder_Kanisa La Neema | funder_Kanisa La Tag | funder_Kanisani | funder_Kapelo | funder_Karadea Ngo | funder_Kashwas | funder_Kassim | funder_Kata | funder_Kauzeni | funder_Kayempu Ltd | funder_Kc | funder_Kcu | funder_Kdc | funder_Kdpa | funder_Kdrdp Ngo | funder_Kegocha | funder_Kenyans Company | funder_Kerebuka | funder_Kfw | funder_Ki | funder_Kibaha Independent School | funder_Kibaha Town Council | funder_Kibara Foundation | funder_Kibo | funder_Kibo Brewaries | funder_Kidep | funder_Kidika | funder_Kidp | funder_Kigoma Municipal | funder_Kigoma Municipal Council | funder_Kigwa | funder_Kijij | funder_Kijiji | funder_Kikom | funder_Kikundi Cha Akina Mama | funder_Kilimarondo Parish | funder_Kilimo | funder_Kilindi District Co | funder_Kiliwater | funder_Killflora | funder_Kilol | funder_Kilomber | funder_Kilwater | funder_Kimkuma | funder_Kinapa | funder_Kindoroko Water Project | funder_Kinga | funder_Kingupira S | funder_Kipo Potry | funder_Kirde | funder_Kirdep | funder_Kitiangare Village Community | funder_Kiuma | funder_Kiwanda Cha Ngozi | funder_Kiwanda Cha Samaki | funder_Kiwanda Cha Tangawizi | funder_Kizego Jumaa | funder_Kizenga | funder_Kkkt | funder_Kkkt Canal | funder_Kkkt Church | funder_Kkkt Dme | funder_Kkkt Leguruki | funder_Kkkt Mareu | funder_Kkkt Ndrumangeni | funder_Kkkt Usa | funder_Kkkt-dioces Ya Pare | funder_Kkkt_makwale | funder_Kmcl | funder_Kmt | funder_Koica | funder_Koica And Tanzania Government | funder_Koico | funder_Kokornel | funder_Kolopin | funder_Kombe Foundation | funder_Kome Parish | funder_Kondela | funder_Kondo Primary | funder_Konoike | funder_Kopwe Khalifa | funder_Korea | funder_Krp | funder_Ku | funder_Kuamu | funder_Kuji Foundation | funder_Kurrp | funder_Kurrp Ki | funder_Kuwait | funder_Kuwasa | funder_Kwa Ditriki Cho | funder_Kwa Makala | funder_Kwa Mzee Waziri | funder_Kwamdulu Estate | funder_Kwang-nam Middle-school | funder_Kwaruhombo He | funder_Kwasenenge Group | funder_Kwik | funder_Kwikwiz | funder_Kyariga | funder_Kyela Council | funder_Kyela-morogoro | funder_L | funder_Laizer | funder_Lake Tanganyika | funder_Lake Tanganyika Basin | funder_Lake Tanganyika Prodap | funder_Lamp | funder_Laramatak | funder_Latfu | funder_Lawate Fuka Water Suppl | funder_Lawatefuka Water Supply | funder_Lc | funder_Lcdg | funder_Lcgd | funder_Ldcdd | funder_Ldcgd | funder_Lee Kang Pyung's Family | funder_Legeza Legeza | funder_Lench | funder_Lench Taramai | funder_Leopad Abeid | funder_Lg | funder_Lga | funder_Lga And Adb | funder_Lgcbg | funder_Lgcd | funder_Lgcdg | funder_Lgcgd | funder_Lgdbg | funder_Lgdcg | funder_Lidep | funder_Lifetime | funder_Lion Clu | funder_Lions | funder_Lions C | funder_Lions Club | funder_Lions Club Kilimanjaro | funder_Lips | funder_Lisa | funder_Liuwassa | funder_Livin | funder_Living Water International | funder_Liz | funder_Lizad | funder_Local | funder_Loliondo Parish | funder_Loliondo Secondary | funder_Long Ga | funder_Longido Sec School | funder_Loocip | funder_Losaa-kia Water Supply | funder_Losakia Water Supply | funder_Lotary Club | funder_Lotary International | funder_Lottery | funder_Lottery Club | funder_Louise Elucas Sala | funder_Lowasa | funder_Luali Kaima | funder_Luchelegu Primary School | funder_Luka | funder_Luke Samaras Ltd | funder_Lungwe | funder_Lusajo | funder_Luthe | funder_Lutheran | funder_Lutheran Church | funder_Lvemp | funder_Lvia | funder_Lwf | funder_Lwi | funder_Lwi & Central Government | funder_Lwiji Italy | funder_M | funder_M And P | funder_Ma | funder_Maajabu Pima | funder_Maashumu Mohamed | funder_Mac | funder_Machibya Guma | funder_Madaraweshi | funder_Madra | funder_Maerere | funder_Mafwimbo | funder_Magadini Makiwaru Water | funder_Magadini-makiwaru Water | funder_Magani | funder_Magereza | funder_Magige | funder_Magoma Adp | funder_Magu Food Security | funder_Magul | funder_Magutu Maro | funder_Mahemba | funder_Mahita | funder_Majengo Prima | funder_Maji Mugumu | funder_Maju Mugumu | funder_Makanga | funder_Makanya Sisal Estate | funder_Makapuchini | funder_Makli | funder_Makombo | funder_Makona | funder_Makondakonde Water Population | funder_Makonde | funder_Makonde Water Population | funder_Makonde Water Supply | funder_Makonder | funder_Makori | funder_Makoye Masanzu | funder_Makundya | funder_Makuru | funder_Makusa | funder_Malec | funder_Males | funder_Maliasili | funder_Malola | funder_Mama Ku | funder_Mama Mery Nagu | funder_Mamad | funder_Mamaz | funder_Mambe | funder_Mamlaka Ya Maji Ngara | funder_Mamvua Kakungu | funder_Mango Tree | funder_Manyota Primary School | funder_Manyovu Agriculture Institute | funder_Marafin | funder_Marafip | funder_Marke | funder_Maro | funder_Maro Kyariga | funder_Marumbo Community | funder_Masai Land | funder_Maseka Community | funder_Masese | funder_Mashaka | funder_Masista | funder_Maswi Drilling Co. Ltd | funder_Mataro | funder_Matata Selemani | funder_Matimbwa Sec | funder_Matogoro | funder_Matyenye | funder_Mavuno Ngo | funder_Maxavella | funder_Mayiro | funder_Mazaro Kabula | funder_Mbeje | funder_Mbiusa | funder_Mbiuwasa | funder_Mboma | funder_Mboni Salehe | funder_Mbozi District Council | funder_Mbozi Hospital | funder_Mbozi Secondary School | funder_Mbunge | funder_Mbuzi Mawe | funder_Mbwana Omari | funder_Mbwiro | funder_Mchukwi Hos | funder_Md | funder_Mdc | funder_Mdgwc | funder_Mdrdp | funder_Meco | funder_Medicine | funder_Meko Balo | funder_Mem | funder_Member O | funder_Member Of Parliament | funder_Member Of Perliament Ahmed Ali | funder_Menon | funder_Meru Concrete | funder_Methodist Church | funder_Mfuko Wa Jimbo | funder_Mfuko Wa Jimbo La Magu | funder_Mganga | funder_Mgaya | funder_Mgaya Masese | funder_Mgm | funder_Mh An | funder_Mh Kapuya | funder_Mh.Chiza | funder_Mh.J S Sumari | funder_Mheza Distric Counc | funder_Mhina | funder_Mhoranzi | funder_Mhuzu | funder_Mi | funder_Miab | funder_Mianz | funder_Mikumi G | funder_Milenia | funder_Mileniam Project | funder_Millenium | funder_Minis | funder_Ministry Of Agricultura | funder_Ministry Of Education | funder_Ministry Of Healthy | funder_Ministry Of Water | funder_Minjingu | funder_Miomb | funder_Misana George | funder_Misheni | funder_Misri Government | funder_Missi | funder_Missio | funder_Mission | funder_Missionaries | funder_Missionary | funder_Mitema | funder_Miziriol | funder_Mkinga Distric Cou | funder_Mkinga Distric Coun | funder_Mkulima | funder_Mkuluku | funder_Mkurugenzi | funder_Mkuyu | funder_Mmanya Abdallah | funder_Mmem | funder_Mmg Gold Mine | funder_Mnyama | funder_Mnyambe | funder_Mnyamisi Jumaa | funder_Morad | funder_Moradi | funder_Moravian | funder_Moroil | funder_Morovian | funder_Morovian Church | funder_Morrovian | funder_Moses | funder_Moshono Adp | funder_Moslem Foundation | funder_Mosque | funder_Mosqure | funder_Motiba Manyanya | funder_Motiba Wambura | funder_Mow | funder_Moyowosi Basin | funder_Mp | funder_Mp Mloka | funder_Mp Mzeru | funder_Mrtc | funder_Ms | funder_Ms-danish | funder_Msabi | funder_Msf | funder_Msf/tacare | funder_Msigw | funder_Msigwa | funder_Msiki | funder_Msikiti | funder_Msikiti Masji | funder_Msikitini | funder_Mstiiti | funder_Msudi | funder_Mtambo | funder_Mtc | funder_Mtewe | funder_Mtibwa S | funder_Mtuwasa | funder_Mtuwasa And Community | funder_Muhameid Na | funder_Muhindi | funder_Muhochi Kissaka | funder_Muislam | funder_Muivaru | funder_Mungaya | funder_Municipal Council | funder_Muniko | funder_Musilim Agency | funder_Muslim Society | funder_Muslim World | funder_Muslimehefen International | funder_Muslims | funder_Muslimu Society(shia) | funder_Muwasa | funder_Muwsa | funder_Mwakabalula | funder_Mwakalinga | funder_Mwakifuna | funder_Mwalimu Maneromango Muhenzi | funder_Mwalimu Muhenza | funder_Mwalimu Omari | funder_Mwamama | funder_Mwamvita Rajabu | funder_Mwanaisha Mwidadi | funder_Mwanamisi Ally | funder_Mwanga Town Water Authority | funder_Mwanza | funder_Mwaya Mn | funder_Mwelia Estate | funder_Mwigicho | funder_Mwingereza | funder_Mwinjuma Mzee | funder_Mwita | funder_Mwita Kichere | funder_Mwita Lucas | funder_Mwita Machota | funder_Mwita Mahiti | funder_Mwita Muremi | funder_Mwl. Nyerere Sec. 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waterpoint_type_dam | waterpoint_type_hand pump | waterpoint_type_improved spring | waterpoint_type_other |
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from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
train_data_1[num_col_sc] = sc.fit_transform(train_data_1[num_col_sc])
/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler. return self.partial_fit(X, y) /anaconda3/lib/python3.6/site-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler. return self.fit(X, **fit_params).transform(X)
train_data_1.head()
| amount_tsh | gps_height | population | years_elapsed | basin_pump_dist | funder_0 | funder_A/co Germany | funder_Aar | funder_Abas Ka | funder_Abasia | funder_Abc-ihushi Development Cent | funder_Abd | funder_Abdala | funder_Abddwe | funder_Abdul | funder_Abood | funder_Abs | funder_Aco/germany | funder_Acord | funder_Acord Ngo | funder_Acra | funder_Act | funder_Act Mara | funder_Action Aid | funder_Action Contre La Faim | funder_Action In A | funder_Adap | funder_Adb | funder_Adf | funder_Adp | funder_Adp Bungu | funder_Adp Mombo | funder_Adp/w | funder_Adra | funder_Af | funder_Afdp | funder_Afric | funder_Africa | funder_Africa 2000 Network/undp | funder_Africa Amini Alama | funder_Africa Project Ev Germany | funder_African | funder_African 2000 Network | funder_African Barrick Gold | funder_African Development Bank | funder_African Development Foundation | funder_African Muslim Agency | funder_African Realief Committe Of Ku | funder_African Reflections Foundation | funder_African Relie | funder_Africaone Ltd | funder_Africare | funder_Afriican Reli | funder_Afroz Ismail | funder_Afya Department Lindi Rural | funder_Agape Churc | funder_Agt Church | funder_Ahmadia | funder_Ai | funder_Aic | funder_Aic Church | funder_Aic Kij | funder_Aict | funder_Aimgold | funder_Aixos | funder_Alia | funder_Ambwene Mwaikek | funder_Amref | funder_Amrefe | funder_Anglican Church | funder_Angrikana | funder_Anjuman E Seifee | funder_Answeer Muslim Grou | funder_Apm | funder_Apm[africa Precious Metals Lt | funder_Aqua Blues Angels | funder_Arab Community | funder_Arabi | funder_Arabs Community | funder_Ardhi Instute | funder_Area | funder_Artisan | funder_Asb | funder_Asdp | funder_Asgerali N Bharwan | funder_Auwasa | funder_Awf | funder_B.A.P | funder_Ba As | funder_Babtest | funder_Babtist | funder_Bahewasa | funder_Bahresa | funder_Bakari Chimkube | funder_Bakwata | funder_Ballo | funder_Balo | funder_Balyehe | funder_Banca Reale | funder_Bank | funder_Bao | funder_Baptist Church | funder_Baric | funder_Bathlomew Vicent | funder_Batist Church | funder_Belgian Government | funder_Belgij | funder_Bened | funder_Benguka | funder_Bffs | funder_Bfwd | funder_Bgm | funder_Bgss | funder_Bgssws | funder_Bhws | funder_Bilila | funder_Bingo Foundation | funder_Bingo Foundation Germany | funder_Bio Fuel Company | funder_Biore | funder_Birage | funder_Bkhws | funder_Boazi | funder_Boazi /o | funder_Bobby | funder_Bokera W | funder_Boma Saving | funder_Bong-kug Ohh/choonlza Lee | funder_Bonite Bottles Ltd | funder_Br | funder_Bra | funder_Brad | funder_Brdp | funder_Bread For The Wor | funder_Bread Of The Worl | funder_Bridge North | funder_British Colonial Government | funder_British Tanza | funder_Brown | funder_Bruder | funder_Bs | funder_Bsf | funder_Bukumbi | funder_Bukwang Church Saint | funder_Bukwang Church Saints | funder_Buluga Subvillage Community | funder_Bulyahunlu Gold Mine | funder_Bumabu | funder_Buptist | funder_Busoga Trust | funder_C | funder_Cafod | funder_Caltas | funder_Caltas Tanzania | funder_Caltaz Kahama | funder_Caltus | funder_Calvary Connect | funder_Camartec | funder_Camavita | funder_Canada | funder_Canada Aid | funder_Care Int | funder_Care International | funder_Care/cipro | funder_Care/dwe | funder_Caritas | funder_Carmatech | funder_Cartas Tanzania | funder_Cast | funder_Cathoric | funder_Cbhi | funder_Cc Motor Day 2010 | funder_Ccp | funder_Ccpk | funder_Ccps | funder_Cct | funder_Cdcg | funder_Cdft | funder_Cdg | funder_Cdtf | funder_Cdtfdistrict Council | funder_Cefa | funder_Cefa-njombe | funder_Cefa/rcchurch | funder_Ces (gmbh) | funder_Ces(gmbh) | funder_Cg | funder_Cg/rc | funder_Cgc | funder_Cgi | funder_Ch | funder_Chacha | funder_Chacha Issame | funder_Chai Wazir | funder_Chama Cha Ushirika | funder_Chamavita | funder_Chani | funder_Charlotte Well | funder_Cheni | funder_China Government | funder_Chmavita | funder_Chongolo | funder_Christan Outrich | funder_Christian Outrich | funder_Chuo | funder_Churc | funder_Church | funder_Church Of Disciples | funder_Cida | funder_Cip | funder_Cipro | funder_Cipro/care | funder_Cipro/care/tcrs | funder_Cipro/government | funder_Clause | funder_Cmcr | funder_Cmsr | funder_Co | funder_Cobashec | funder_Cocen | funder_Cocern | funder_Cocu | funder_College | funder_Colonial Government | funder_Commu | funder_Community | funder_Community Bank | funder_Compa | funder_Company | funder_Compasion International | funder_Comune Di Roma | funder_Comunedi Roma | funder_Comunity Construction Fund | funder_Conce | funder_Concen | funder_Concern | funder_Concern /govern | funder_Concern World Wide | funder_Concern/governm | funder_Cope | funder_Costantine Herman | funder_Council | funder_Cpar | funder_Cper | funder_Cpps | funder_Cpps Mission | funder_Cpro | funder_Craelius | funder_Cristan Outrich | funder_Crs | funder_Csf | funder_Cspd | funder_Cvs Miss | funder_D | funder_D Ct | funder_Da Unoperaio Siciliano | funder_Dacp | funder_Dadid | funder_Dadis | funder_Dadp | funder_Dads | funder_Dae Yeol And Chae Lynn | funder_Dagida | funder_Daida | funder_Dak | funder_Daldo | funder_Danida | funder_Danida /government | funder_Dar Al Ber | funder_Dar Es Salaam Round Table | funder_Dasiip | funder_Dasip | funder_Dasp | funder_Dasp Ltd | funder_Dassip | funder_Dawasa | funder_Dawasco | funder_Dbfpe | funder_Dbsp | funder_Dbspe | funder_Dct | funder_Ddca | funder_Ddp | funder_De | funder_Ded | funder_Ded Kilo | funder_Ded/rwssp | funder_Ded_rwsp | funder_Denat | funder_Denish | funder_Deogratius Kasima | funder_Desk And Chair Foundation | funder_Devon Aid Korogwe | funder_Dfid | funder_Dgv | funder_Dh | funder_Dhinu | funder_Dhv | funder_Dhv Moro | funder_Dhv/gove | funder_Dhv\norp | funder_Dhv\swis | funder_Dimon | funder_Dina | funder_Dioce | funder_Diocese Of Geita | funder_Diocese Of Mount Kilimanjaro | funder_District Council | funder_District Medical | funder_District Rural Project | funder_Diwani | funder_Dmd | funder_Dmdd | funder_Dmdd/solider | funder_Dmk | funder_Dmk Anglican | funder_Dmmd | funder_Dmo | funder_Do | funder_Doctor Mwambi | funder_Doddea | funder_Dokta Mwandulam | funder_Dom | funder_Domestic Rural Development Pr | funder_Domestic Rural Development Pro | funder_Domestic Water Supply Project | funder_Dominiki Simwen | funder_Doner And Com | funder_Doner And Ded | funder_Donor | funder_Dqnida | funder_Drdp | funder_Drdp Ngo | funder_Drv Na Idara | funder_Drwssp | funder_Dsdp | funder_Dsp | funder_Duka | funder_Duwas | funder_Dv | funder_Dw | funder_Dwarf | funder_Dwe | funder_Dwe And Veo | funder_Dwe/anglican Church | funder_Dwe/bamboo Projec | funder_Dwe/norad | funder_Dwe/rudep | funder_Dwe/ubalozi Wa Marekani | funder_Dwsdp | funder_Dwsp | funder_Dwssp | funder_Dwst | funder_Dwt | funder_Ea | funder_Eastmeru Medium School | funder_Eater | funder_Ebaha | funder_Eco Lodge | funder_Education Funds | funder_Efg | funder_Egypt | funder_Egypt Government | funder_Egypt Technical Co Operation | funder_El | funder_Elca | funder_Elct | funder_Embasy Of Japan In Tanzania | funder_Engin | funder_Engineers Without Border | funder_Eno | funder_Enyuati | funder_Enyueti | funder_Ereto | funder_Ermua | funder_Erre Kappa | funder_Esawa | funder_Ester Ndege | funder_Eu | funder_Eu/acra | funder_Eung Am Methodist Church | funder_Eung-am Methodist Church | funder_European Union | funder_F | funder_Fabia | funder_Fao | funder_Farm Africa | funder_Farm-africa | funder_Fathe | funder_Father Bonifasi | funder_Father W | funder_Fdc | funder_Fida | funder_Filo | funder_Fin Water | funder_Fini Water | funder_Finida German Tanzania Govt | funder_Finidagermantanzania Govt | funder_Finland | funder_Finland Government | funder_Finn Water | funder_Finw | funder_Finwater | funder_Fiwater | funder_Floresta | funder_Folac | funder_Foreigne | funder_Fosecu | funder_Fpct | funder_Fpct Church | funder_Fpct Mulala | funder_Fptc - Pent | funder_Franc | funder_France | funder_Frankfurt | funder_Fredked Conservation | funder_Free Pentecoste Church Of Tanz | funder_Fresh Water Plc England | funder_Friedkin Conservation Fund | funder_Friend From Un | funder_Friends Of Kibara Foundation | funder_Friends Of Ulambo And Mwanhala | funder_Full Gospel Church | funder_Fw | funder_G.D&i.D | funder_Ga | funder_Gachuma Ginery | funder_Gaica | funder_Gain | funder_Game Division | funder_Game Fronti | funder_Gdp | funder_Geita Goldmain | funder_Gen | funder_Geochaina | funder_Gerald Tuseko Gro | funder_German Missionary | funder_Germany | funder_Germany Cristians | funder_Germany Misionary | funder_Germany Missionary | funder_Germany Republi | funder_Gesawa | funder_Getdsc00 | funder_Getekwe | funder_Gg | funder_Ggm | funder_Gil Cafe'church' | funder_Giovan Disinistra Per Salve | funder_Giz | funder_Global Fund | funder_Go | funder_Godii | funder_Goldmain | funder_Goldwill Foundation | funder_Government | funder_Government /sda | funder_Government /tassaf | funder_Government /world Vision | funder_Government And Community | funder_Government Of Misri | funder_Government Of Tanzania | funder_Government/ Community | funder_Government/ World Bank | funder_Government/school | funder_Government/tassaf | funder_Government/tcrs | funder_Gra Na Halmashauri | funder_Grail Mission Kiseki Bar | funder_Grazie Franco Lucchini | funder_Grazie Grouppo Padre Fiorentin | funder_Greec | funder_Greinaker | funder_Greineker | funder_Grumeti | funder_Gt | funder_Gtz | funder_Gurdians | funder_Gwitembe | funder_H | funder_H/w | funder_H4ccp | funder_Haam | funder_Haidomu Lutheran Church | funder_Halimashau | funder_Halimashauli | funder_Halmashauli | funder_Halmashaur | funder_Halmashauri | funder_Halmashauri Wil | funder_Halmashauri Ya Manispa Tabora | funder_Halmashauri Ya Wilaya | funder_Halmashauri Ya Wilaya Sikonge | funder_Ham | funder_Hamref | funder_Handeni Trunk Main( | funder_Handeni Trunk Maini | funder_Hans | funder_Hapa | funder_Hapa Singida | funder_Happy Watoto Foundation | funder_Haruna Mpog | funder_Hasawa | funder_Hashi | funder_Hasnan Murig (mbunge) | funder_Hasnein Muij Mbunge | funder_Hasnein Murij | funder_Hassan Gulam | funder_Haydom Lutheran Hospital | funder_Hdv | funder_He | funder_Healt | funder_Health Ministry | funder_Hearts Helping Hands.Inc. | funder_Henure Dema | funder_Heri Mission | funder_Hery | funder_Hesaw | funder_Hesawa | funder_Hesawa And Concern World Wide | funder_Hesawwa | funder_Hesawz | funder_Hesawza | funder_Hesswa | funder_Hewasa | funder_Hewawa | funder_Hez | funder_Hhesawa | funder_Hiap | funder_Hifab | funder_Hilfe Fur Brunder | funder_Hindu | funder_Holand | funder_Holili Water Supply | funder_Holla | funder_Holland | funder_Hongoli | funder_Hortanzia | funder_Hospital | funder_Hotels And Lodge Tanzania | funder_Hotels And Loggs Tz Ltd | funder_Hpa | funder_Hsw | funder_Htm | funder_Huches | funder_Hw/rc | funder_Hydom Luthelani | funder_I Wash | funder_I.E.C | funder_Iado | funder_Icap | funder_Icdp | funder_Icf | funder_Ics | funder_Idara Ya Maji | funder_Idc | funder_Idea | funder_Idf | funder_Idydc | funder_If | funder_Ifad | funder_Ifakara | funder_Igolola Community | funder_Ikela Wa | funder_Ikeuchi Towels Japan | funder_Il | funder_Ilaramataki | funder_Ilct | funder_Ilkeri Village | funder_Ilo | funder_Ilo/undp | funder_Ilwilo Community | funder_Imf | funder_In | funder_In Memoria Di Albeto | funder_Incerto | funder_Inkinda | funder_Insititutiona | funder_Institution | funder_Institutional | funder_Insututional | funder_Internal Drainage Basin | funder_International Aid Services | funder_Investor | funder_Iom | funder_Ir | funder_Iran Gover | funder_Irc | funder_Irevea Sister | funder_Irevea Sister Water | funder_Irish Ai | funder_Irish Government | funder_Is | funder_Isf | funder_Isf / Tasaff | funder_Isf/government | funder_Isf/gvt | funder_Isf/tacare | funder_Isingiro Ho | funder_Islam | funder_Islamic | funder_Islamic Agency Tanzania | funder_Islamic Community | funder_Islamic Found | funder_Islamic Society | funder_Isnashia And | funder_Issa Mohamedi Tumwanga | funder_Italian | funder_Italy | funder_Italy Government | funder_Iucn | funder_Jacobin | funder_Jafary Mbaga | funder_Jaica | funder_Jamal | funder_Jamal Abdallah | funder_Japan | funder_Japan Food Aid Counter Part | funder_Japan Aid | funder_Japan Embassy | funder_Japan Food | funder_Japan Food Aid | funder_Japan Government | funder_Jbg | funder_Jeica | funder_Jeshi La Wokovu | funder_Jeshi La Wokovu [cida] | funder_Jeshi Lawokovu | funder_Jgb | funder_Jica | funder_Jika | funder_Jimbo Fund | funder_Jimmy | funder_Jipa | funder_John Fund | funder_John Gileth | funder_John Skwese | funder_Ju | funder_Ju-sarang Church' And Bugango | funder_Judge Mchome | funder_Juhibu | funder_Juma | funder_Jumaa | funder_Jumanne | funder_Jumanne Siabo | funder_Justine Marwa | funder_Jwtz | funder_K | funder_Ka | funder_Kaaya | funder_Kadip | funder_Kadp | funder_Kadres Ngo | funder_Kaemp | funder_Kagera | funder_Kagera Mine | funder_Kagunguli Secondary | funder_Kahema | funder_Kajima | funder_Kalebejo Parish | funder_Kalitasi | funder_Kalitesi | funder_Kalta | funder_Kamama | funder_Kamata Project | funder_Kambi Migoko | funder_Kanamama | funder_Kando | funder_Kanis | funder_Kanisa | funder_Kanisa Katoliki | funder_Kanisa Katoliki Lolovoni | funder_Kanisa La Menonite | funder_Kanisa La Mitume | funder_Kanisa La Neema | funder_Kanisa La Tag | funder_Kanisani | funder_Kapelo | funder_Karadea Ngo | funder_Kashwas | funder_Kassim | funder_Kata | funder_Kauzeni | funder_Kayempu Ltd | funder_Kc | funder_Kcu | funder_Kdc | funder_Kdpa | funder_Kdrdp Ngo | funder_Kegocha | funder_Kenyans Company | funder_Kerebuka | funder_Kfw | funder_Ki | funder_Kibaha Independent School | funder_Kibaha Town Council | funder_Kibara Foundation | funder_Kibo | funder_Kibo Brewaries | funder_Kidep | funder_Kidika | funder_Kidp | funder_Kigoma Municipal | funder_Kigoma Municipal Council | funder_Kigwa | funder_Kijij | funder_Kijiji | funder_Kikom | funder_Kikundi Cha Akina Mama | funder_Kilimarondo Parish | funder_Kilimo | funder_Kilindi District Co | funder_Kiliwater | funder_Killflora | funder_Kilol | funder_Kilomber | funder_Kilwater | funder_Kimkuma | funder_Kinapa | funder_Kindoroko Water Project | funder_Kinga | funder_Kingupira S | funder_Kipo Potry | funder_Kirde | funder_Kirdep | funder_Kitiangare Village Community | funder_Kiuma | funder_Kiwanda Cha Ngozi | funder_Kiwanda Cha Samaki | funder_Kiwanda Cha Tangawizi | funder_Kizego Jumaa | funder_Kizenga | funder_Kkkt | funder_Kkkt Canal | funder_Kkkt Church | funder_Kkkt Dme | funder_Kkkt Leguruki | funder_Kkkt Mareu | funder_Kkkt Ndrumangeni | funder_Kkkt Usa | funder_Kkkt-dioces Ya Pare | funder_Kkkt_makwale | funder_Kmcl | funder_Kmt | funder_Koica | funder_Koica And Tanzania Government | funder_Koico | funder_Kokornel | funder_Kolopin | funder_Kombe Foundation | funder_Kome Parish | funder_Kondela | funder_Kondo Primary | funder_Konoike | funder_Kopwe Khalifa | funder_Korea | funder_Krp | funder_Ku | funder_Kuamu | funder_Kuji Foundation | funder_Kurrp | funder_Kurrp Ki | funder_Kuwait | funder_Kuwasa | funder_Kwa Ditriki Cho | funder_Kwa Makala | funder_Kwa Mzee Waziri | funder_Kwamdulu Estate | funder_Kwang-nam Middle-school | funder_Kwaruhombo He | funder_Kwasenenge Group | funder_Kwik | funder_Kwikwiz | funder_Kyariga | funder_Kyela Council | funder_Kyela-morogoro | funder_L | funder_Laizer | funder_Lake Tanganyika | funder_Lake Tanganyika Basin | funder_Lake Tanganyika Prodap | funder_Lamp | funder_Laramatak | funder_Latfu | funder_Lawate Fuka Water Suppl | funder_Lawatefuka Water Supply | funder_Lc | funder_Lcdg | funder_Lcgd | funder_Ldcdd | funder_Ldcgd | funder_Lee Kang Pyung's Family | funder_Legeza Legeza | funder_Lench | funder_Lench Taramai | funder_Leopad Abeid | funder_Lg | funder_Lga | funder_Lga And Adb | funder_Lgcbg | funder_Lgcd | funder_Lgcdg | funder_Lgcgd | funder_Lgdbg | funder_Lgdcg | funder_Lidep | 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col_lst = train_data_1.columns
col_lst
Index(['amount_tsh', 'gps_height', 'population', 'years_elapsed',
'basin_pump_dist', 'funder_0', 'funder_A/co Germany', 'funder_Aar',
'funder_Abas Ka', 'funder_Abasia',
...
'source_shallow well', 'source_spring', 'source_unknown',
'waterpoint_type_cattle trough', 'waterpoint_type_communal standpipe',
'waterpoint_type_communal standpipe multiple', 'waterpoint_type_dam',
'waterpoint_type_hand pump', 'waterpoint_type_improved spring',
'waterpoint_type_other'],
dtype='object', length=4156)
# dropping one column each from the group of newly created dummy columns to ward off the issue of multi-collinearity
train_data_1.drop(['funder_funder_missing','installer_installer_missing','region_Iringa',
'public_meeting_miss_p_mtng','scheme_management_miss_sch_mngt','permit_miss_permit',
'management_other - school','payment_unknown','water_quality_unknown',
'quantity_unknown','source_unknown','water_quality_unknown'], axis=1, inplace=True)
train_data_1.shape
(59400, 4145)
# import the train-target dataset
train_target = pd.read_csv('raw_train_target_vectors.csv')
train_target.shape
(59400, 2)
train_target.head()
| id | status_group | |
|---|---|---|
| 0 | 69572 | functional |
| 1 | 8776 | functional |
| 2 | 34310 | functional |
| 3 | 67743 | non functional |
| 4 | 19728 | functional |
train_target['status_group'].isna().sum()
0
train_target['status_group'].unique()
array(['functional', 'non functional', 'functional needs repair'],
dtype=object)
train_target['status_group'] = train_target['status_group'].map({'functional':0,
'non functional':1,
'functional needs repair':2})
train_target.head()
0 0 1 0 2 0 3 1 4 0 Name: status_group, dtype: int64
train_target = train_target['status_group']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train_data_1,train_target,test_size=.15,
random_state=321)
## get validation set from train set
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=.2,
random_state=313)
X_train.shape, X_valid.shape, X_test.shape, y_train.shape, y_valid.shape, y_test.shape
((40392, 4145), (10098, 4145), (8910, 4145), (40392,), (10098,), (8910,))
from sklearn.decomposition import PCA
pca = PCA(.95)
pca.fit(X_train)
#pca_samples = pca.transform(train_data_1)
PCA(copy=True, iterated_power='auto', n_components=0.95, random_state=None, svd_solver='auto', tol=0.0, whiten=False)
pca.n_components_
136
pca.explained_variance_ratio_
array([0.12822833, 0.0892335 , 0.08197641, 0.07738054, 0.05933666,
0.04592652, 0.03761974, 0.03254067, 0.0292746 , 0.02666826,
0.02030454, 0.0169611 , 0.01622157, 0.01521431, 0.01304298,
0.01219111, 0.011536 , 0.01099073, 0.0098409 , 0.00953179,
0.00884177, 0.00763352, 0.00735443, 0.00729742, 0.00657773,
0.00595982, 0.0057316 , 0.00516616, 0.00498214, 0.00491715,
0.00451783, 0.00436116, 0.00418003, 0.00390396, 0.00381815,
0.00374392, 0.00365451, 0.00358327, 0.00350678, 0.00329978,
0.00309766, 0.00299255, 0.0029528 , 0.00282128, 0.00278156,
0.00265938, 0.00261969, 0.00258057, 0.00243823, 0.0023699 ,
0.00224707, 0.00218801, 0.00215508, 0.00204343, 0.0020147 ,
0.00194403, 0.00188566, 0.0018381 , 0.00178667, 0.0016899 ,
0.00163151, 0.00160954, 0.00158515, 0.00154052, 0.00146876,
0.00143221, 0.00141691, 0.00138489, 0.00134239, 0.00131341,
0.00129063, 0.00126102, 0.00116261, 0.00112661, 0.00111006,
0.0010919 , 0.00107895, 0.00101405, 0.00100175, 0.00096405,
0.00094858, 0.00089288, 0.00086306, 0.00084718, 0.00080482,
0.0007981 , 0.0007848 , 0.000781 , 0.00075451, 0.00075242,
0.00072239, 0.00069709, 0.00068323, 0.00067332, 0.00065102,
0.00064152, 0.00062645, 0.00059968, 0.00059874, 0.00057203,
0.00056631, 0.00055281, 0.00053592, 0.00053434, 0.00051778,
0.00051372, 0.0005058 , 0.00048885, 0.00048145, 0.00047797,
0.00047221, 0.00046802, 0.00046309, 0.00045528, 0.00043991,
0.00043818, 0.00043685, 0.00042353, 0.00041998, 0.00041386,
0.00040765, 0.00040508, 0.00040124, 0.00039463, 0.00039142,
0.00038606, 0.000385 , 0.00038283, 0.00037702, 0.00037091,
0.00036269, 0.00035417, 0.00034932, 0.00034282, 0.00033756,
0.00033566])
for n_compnt in [50,60,70,75,80,85,90,95,100,110,120,125,130,135]:
print('{} % of variance is explained by first {} components.'
.format((round(np.cumsum(pca.explained_variance_ratio_)[n_compnt],2) * 100),n_compnt))
88.0 % of variance is explained by first 50 components. 90.0 % of variance is explained by first 60 components. 91.0 % of variance is explained by first 70 components. 92.0 % of variance is explained by first 75 components. 92.0 % of variance is explained by first 80 components. 93.0 % of variance is explained by first 85 components. 93.0 % of variance is explained by first 90 components. 93.0 % of variance is explained by first 95 components. 94.0 % of variance is explained by first 100 components. 94.0 % of variance is explained by first 110 components. 94.0 % of variance is explained by first 120 components. 95.0 % of variance is explained by first 125 components. 95.0 % of variance is explained by first 130 components. 95.0 % of variance is explained by first 135 components.
X_train = pca.transform(X_train)
X_valid = pca.transform(X_valid)
X_test = pca.transform(X_test)
X_train.shape , X_valid.shape, X_test.shape
((40392, 136), (10098, 136), (8910, 136))
X_train[:5,:5]
array([[ 0.09558042, 0.20629677, 1.10368332, -1.43542895, -1.27141352],
[-1.56349914, -0.07807022, 0.06527295, 0.92176458, -0.39106082],
[ 0.8930848 , -0.27636336, 0.90167219, -0.51384158, 0.07970041],
[ 0.9443107 , -0.06926744, -0.70016221, -0.05104971, 1.31398127],
[-0.72705389, -0.12687915, -0.72443054, 1.20887384, 0.58719472]])
from sklearn.metrics import accuracy_score
from time import time
def probable_models(classifier, train_ftr_set, valid_ftr_set, train_trgt_set, valid_trgt_set):
'''
defining a function to iterative run over a list of classifiers and generate a
comparison matrix
'''
result = {}
start_tm = time()
classifier = classifier.fit(train_ftr_set, train_trgt_set)
end_tm = time()
result['train_score'] = classifier.score(train_ftr_set, train_trgt_set)
result['valid_score'] = classifier.score(valid_ftr_set, valid_trgt_set)
result['time'] = end_tm - start_tm
start_tm = 0
end_tm = 0
return result
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier
from sklearn.neural_network import MLPClassifier
clf_a = SVC(random_state = 313)
clf_b = GaussianNB()
clf_c = KNeighborsClassifier()
clf_d = DecisionTreeClassifier(random_state = 313)
clf_e = RandomForestClassifier(criterion='entropy', random_state = 313)
clf_f = AdaBoostClassifier(random_state = 313)
clf_g = GradientBoostingClassifier(random_state = 313)
clf_h = ExtraTreesClassifier(criterion='entropy', random_state=313)
clf_i = MLPClassifier(random_state = 313)
results = {}
for clf in [clf_a, clf_b, clf_c, clf_d, clf_e, clf_f, clf_g, clf_h, clf_i]:
name = clf.__class__.__name__
results[name] = probable_models(clf, X_train, X_valid, y_train, y_valid)
/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning. "avoid this warning.", FutureWarning) /anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) /anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning) /anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. % self.max_iter, ConvergenceWarning)
results
{'SVC': {'train_score': 0.7376213111507229,
'valid_score': 0.7370766488413547,
'accuracy': 0.7370766488413547,
'time': 228.85598397254944},
'GaussianNB': {'train_score': 0.4734353337294514,
'valid_score': 0.4726678550207962,
'accuracy': 0.4726678550207962,
'time': 0.10456705093383789},
'KNeighborsClassifier': {'train_score': 0.8353881956823134,
'valid_score': 0.7756981580510992,
'accuracy': 0.7756981580510992,
'time': 0.23358511924743652},
'DecisionTreeClassifier': {'train_score': 0.9974499900970489,
'valid_score': 0.7265795206971678,
'accuracy': 0.7265795206971678,
'time': 11.238741874694824},
'RandomForestClassifier': {'train_score': 0.9784363240245593,
'valid_score': 0.7687660922955041,
'accuracy': 0.7687660922955041,
'time': 6.3978822231292725},
'AdaBoostClassifier': {'train_score': 0.7049168152109329,
'valid_score': 0.7021192315309962,
'accuracy': 0.7021192315309962,
'time': 31.82717800140381},
'GradientBoostingClassifier': {'train_score': 0.7704991087344029,
'valid_score': 0.7625272331154684,
'accuracy': 0.7625272331154684,
'time': 168.3458070755005},
'ExtraTreesClassifier': {'train_score': 0.9974499900970489,
'valid_score': 0.7624282036046742,
'accuracy': 0.7624282036046742,
'time': 1.077476978302002},
'MLPClassifier': {'train_score': 0.8657902554961379,
'valid_score': 0.773717567835215,
'accuracy': 0.773717567835215,
'time': 35.131067991256714}}
model_selection_df = pd.DataFrame(results).T
model_selection_df
| time | train_score | valid_score | |
|---|---|---|---|
| SVC | 229.135942 | 0.737621 | 0.737077 |
| GaussianNB | 0.090633 | 0.473435 | 0.472668 |
| KNeighborsClassifier | 0.224467 | 0.835388 | 0.775698 |
| DecisionTreeClassifier | 11.433602 | 0.997450 | 0.726580 |
| RandomForestClassifier | 6.389280 | 0.978436 | 0.768766 |
| AdaBoostClassifier | 31.773513 | 0.704917 | 0.702119 |
| GradientBoostingClassifier | 167.763767 | 0.770499 | 0.762527 |
| ExtraTreesClassifier | 1.344342 | 0.997450 | 0.762428 |
| MLPClassifier | 64.558635 | 0.865790 | 0.773718 |
t1 = time()
t2 = time()
t2 - t1
7.368470191955566
model_selection_df.T.plot(kind = 'bar', figsize=(17,10))
<matplotlib.axes._subplots.AxesSubplot at 0x1a8300e198>
model_selection_df['time'] = (model_selection_df['time'] - min(model_selection_df['time']))/(max(model_selection_df['time']) - min(model_selection_df['time']))
model_selection_df['train_score'] = (model_selection_df['train_score'] - min(model_selection_df['train_score']))/(max(model_selection_df['train_score']) - min(model_selection_df['train_score']))
model_selection_df['valid_score'] = (model_selection_df['valid_score'] - min(model_selection_df['valid_score']))/(max(model_selection_df['valid_score']) - min(model_selection_df['valid_score']))
model_selection_df
| time | train_score | valid_score | |
|---|---|---|---|
| SVC | 1.000000 | 0.504158 | 0.872549 |
| GaussianNB | 0.000000 | 0.000000 | 0.000000 |
| KNeighborsClassifier | 0.000584 | 0.690730 | 1.000000 |
| DecisionTreeClassifier | 0.049523 | 1.000000 | 0.837908 |
| RandomForestClassifier | 0.027500 | 0.963715 | 0.977124 |
| AdaBoostClassifier | 0.138326 | 0.441746 | 0.757190 |
| GradientBoostingClassifier | 0.732052 | 0.566900 | 0.956536 |
| ExtraTreesClassifier | 0.005474 | 1.000000 | 0.956209 |
| MLPClassifier | 0.281464 | 0.748748 | 0.993464 |
model_selection_df.T.plot(kind='bar', figsize=(17,10))
<matplotlib.axes._subplots.AxesSubplot at 0x1a829b6f60>
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer, accuracy_score
# let's define the parameters dictionary for KNN
'''
parameters = {'n_neighbors' :list(range(20,150,10)),
'algorithm' : ['auto', 'ball_tree', 'kd_tree', 'brute'],
'leaf_size' : list(range(20,150,10)),
'p':[1,2, 3, 4]
}
'''
parameters = {'n_neighbors' :[5],
'algorithm' : ['auto', 'ball_tree', 'kd_tree', 'brute'],
'leaf_size' : [30],
'p':[2]
}
start_tm = time()
scorer = make_scorer(accuracy_score)
clf = KNeighborsClassifier()
grid_obj = GridSearchCV(clf,
param_grid=parameters,
scoring=scorer,
##cv=10,
n_jobs=-1)
grid_fit = grid_obj.fit(X_train, y_train)
best_clf = grid_fit.best_estimator_
base_pred = (clf.fit(X_train, y_train)).predict(X_valid)
best_pred = best_clf.predict(X_valid)
end_tm = time()
print('The prediction by base KNN validation set is : {}'.format(accuracy_score(y_valid, base_pred)),
'\nThe prediction by optimized KNN on validation set is : {}'.format(accuracy_score(y_valid, best_pred)),
'\nTime taken is : {:.4f} sec'.format(end_tm - start_tm))
/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22. warnings.warn(CV_WARNING, FutureWarning)
The prediction by base KNN validation set is : 0.7756981580510992 The prediction by optimized KNN on validation set is : 0.7753020400079224 Time taken is : 409.0240 sec
best_clf.get_params
<bound method BaseEstimator.get_params of KNeighborsClassifier(algorithm='brute', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=5, p=2,
weights='uniform')>
KNeighborsClassifier(algorithm='kd_tree', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=10, p=2,
weights='uniform')
The prediction by base KNN validation set is : 0.7756981580510992
The prediction by optimized KNN on validation set is : 0.7712418300653595
Time taken is : 171.2059 sec
KNeighborsClassifier(algorithm='auto', leaf_size=40, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=10, p=2,
weights='uniform')
The prediction by base KNN validation set is : 0.7756981580510992
The prediction by optimized KNN on validation set is : 0.7713408595761537
Time taken is : 151.4696 sec
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=15, p=2,
weights='uniform')
The prediction by base KNN validation set is : 0.7756981580510992
The prediction by optimized KNN on validation set is : 0.767379679144385
Time taken is : 181.1374 sec
KNeighborsClassifier(algorithm='kd_tree', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=5, p=2,
weights='uniform')
The prediction by base KNN validation set is : 0.7756981580510992
The prediction by optimized KNN on validation set is : 0.7756981580510992
Time taken is : 109.9834 sec
'algorithm' : ['auto', 'ball_tree', 'kd_tree', 'brute'],
KNeighborsClassifier(algorithm='brute', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=5, p=2,
weights='uniform')
The prediction by base KNN validation set is : 0.7756981580510992
The prediction by optimized KNN on validation set is : 0.7753020400079224
Time taken is : 409.0240 sec
from keras.models import Sequential
from keras.layers import Dense, Activation,Dropout
from keras.optimizers import Adam,SGD,RMSprop
from keras import regularizers
from keras.callbacks import ModelCheckpoint
from sklearn.metrics import accuracy_score
from keras.utils import np_utils
X_train.shape
(40392, 136)
nb_classes = 3
y_train = np_utils.to_categorical(y_train, nb_classes)
y_valid = np_utils.to_categorical(y_valid, nb_classes)
y_test = np_utils.to_categorical(y_test, nb_classes)
y_train.shape, y_valid.shape, y_test.shape
((40392, 3), (10098, 3), (8910, 3))
# first model architechture
np.random.seed(13)
model = Sequential()
model.add(Dense(1024, kernel_regularizer=regularizers.l2(.001),input_dim=X_train.shape[1]))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512,kernel_regularizer=regularizers.l2(.001)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(128,kernel_regularizer=regularizers.l1_l2(.001)))
model.add(Activation('relu'))
model.add(Dropout(0.3))
model.add(Dense(32, kernel_regularizer=regularizers.l2(.001)))
model.add(Activation('relu'))
model.add(Dense(3, activation = 'softmax'))
model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_42 (Dense) (None, 1024) 140288 _________________________________________________________________ activation_33 (Activation) (None, 1024) 0 _________________________________________________________________ dropout_25 (Dropout) (None, 1024) 0 _________________________________________________________________ dense_43 (Dense) (None, 512) 524800 _________________________________________________________________ activation_34 (Activation) (None, 512) 0 _________________________________________________________________ dropout_26 (Dropout) (None, 512) 0 _________________________________________________________________ dense_44 (Dense) (None, 128) 65664 _________________________________________________________________ activation_35 (Activation) (None, 128) 0 _________________________________________________________________ dropout_27 (Dropout) (None, 128) 0 _________________________________________________________________ dense_45 (Dense) (None, 32) 4128 _________________________________________________________________ activation_36 (Activation) (None, 32) 0 _________________________________________________________________ dense_46 (Dense) (None, 3) 99 ================================================================= Total params: 734,979 Trainable params: 734,979 Non-trainable params: 0 _________________________________________________________________
# compile the model
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
from keras.callbacks import ModelCheckpoint
# checkpoint
filepath="weights-improvement-best-model.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks_list_1 = [checkpoint]
# training the model
start_time = time()
model.fit(X_train,y_train,batch_size= 10000, epochs= 500,
validation_data=(X_valid,y_valid),
#validation_split = 0.2,
verbose =2,callbacks = callbacks_list_1)
end_time = time()
Train on 40392 samples, validate on 10098 samples Epoch 1/500 - 3s - loss: 6.6574 - acc: 0.4958 - val_loss: 5.6679 - val_acc: 0.6596 Epoch 00001: val_loss improved from inf to 5.66787, saving model to weights-improvement-best-model.hdf5 Epoch 2/500 - 3s - loss: 5.4453 - acc: 0.6364 - val_loss: 4.8705 - val_acc: 0.7239 Epoch 00002: val_loss improved from 5.66787 to 4.87046, saving model to weights-improvement-best-model.hdf5 Epoch 3/500 - 3s - loss: 4.6836 - acc: 0.7190 - val_loss: 4.3321 - val_acc: 0.6666 Epoch 00003: val_loss improved from 4.87046 to 4.33205, saving model to weights-improvement-best-model.hdf5 Epoch 4/500 - 3s - loss: 4.1889 - acc: 0.7001 - val_loss: 3.7808 - val_acc: 0.7408 Epoch 00004: val_loss improved from 4.33205 to 3.78081, saving model to weights-improvement-best-model.hdf5 Epoch 5/500 - 3s - loss: 3.6601 - acc: 0.7374 - val_loss: 3.3519 - val_acc: 0.7490 Epoch 00005: val_loss improved from 3.78081 to 3.35190, saving model to weights-improvement-best-model.hdf5 Epoch 6/500 - 3s - loss: 3.2631 - acc: 0.7323 - val_loss: 2.9859 - val_acc: 0.7444 Epoch 00006: val_loss improved from 3.35190 to 2.98592, saving model to weights-improvement-best-model.hdf5 Epoch 7/500 - 3s - loss: 2.8870 - acc: 0.7387 - val_loss: 2.6629 - val_acc: 0.7411 Epoch 00007: val_loss improved from 2.98592 to 2.66293, saving model to weights-improvement-best-model.hdf5 Epoch 8/500 - 3s - loss: 2.5741 - acc: 0.7358 - val_loss: 2.3390 - val_acc: 0.7483 Epoch 00008: val_loss improved from 2.66293 to 2.33904, saving model to weights-improvement-best-model.hdf5 Epoch 9/500 - 3s - loss: 2.2601 - acc: 0.7476 - val_loss: 2.0643 - val_acc: 0.7495 Epoch 00009: val_loss improved from 2.33904 to 2.06429, saving model to weights-improvement-best-model.hdf5 Epoch 10/500 - 3s - loss: 2.0135 - acc: 0.7413 - val_loss: 1.8560 - val_acc: 0.7372 Epoch 00010: val_loss improved from 2.06429 to 1.85595, saving model to weights-improvement-best-model.hdf5 Epoch 11/500 - 3s - loss: 1.7859 - acc: 0.7457 - val_loss: 1.6394 - val_acc: 0.7424 Epoch 00011: val_loss improved from 1.85595 to 1.63938, saving model to weights-improvement-best-model.hdf5 Epoch 12/500 - 3s - loss: 1.5972 - acc: 0.7398 - val_loss: 1.4530 - val_acc: 0.7535 Epoch 00012: val_loss improved from 1.63938 to 1.45298, saving model to weights-improvement-best-model.hdf5 Epoch 13/500 - 3s - loss: 1.4161 - acc: 0.7474 - val_loss: 1.3495 - val_acc: 0.7273 Epoch 00013: val_loss improved from 1.45298 to 1.34951, saving model to weights-improvement-best-model.hdf5 Epoch 14/500 - 3s - loss: 1.2843 - acc: 0.7425 - val_loss: 1.1681 - val_acc: 0.7566 Epoch 00014: val_loss improved from 1.34951 to 1.16806, saving model to weights-improvement-best-model.hdf5 Epoch 15/500 - 3s - loss: 1.1543 - acc: 0.7460 - val_loss: 1.1005 - val_acc: 0.7500 Epoch 00015: val_loss improved from 1.16806 to 1.10054, saving model to weights-improvement-best-model.hdf5 Epoch 16/500 - 3s - loss: 1.0597 - acc: 0.7500 - val_loss: 1.0057 - val_acc: 0.7459 Epoch 00016: val_loss improved from 1.10054 to 1.00575, saving model to weights-improvement-best-model.hdf5 Epoch 17/500 - 3s - loss: 0.9922 - acc: 0.7433 - val_loss: 0.9442 - val_acc: 0.7480 Epoch 00017: val_loss improved from 1.00575 to 0.94418, saving model to weights-improvement-best-model.hdf5 Epoch 18/500 - 3s - loss: 0.9300 - acc: 0.7468 - val_loss: 0.8732 - val_acc: 0.7602 Epoch 00018: val_loss improved from 0.94418 to 0.87323, saving model to weights-improvement-best-model.hdf5 Epoch 19/500 - 3s - loss: 0.8733 - acc: 0.7547 - val_loss: 0.8418 - val_acc: 0.7569 Epoch 00019: val_loss improved from 0.87323 to 0.84178, saving model to weights-improvement-best-model.hdf5 Epoch 20/500 - 3s - loss: 0.8609 - acc: 0.7461 - val_loss: 0.8543 - val_acc: 0.7436 Epoch 00020: val_loss did not improve from 0.84178 Epoch 21/500 - 3s - loss: 0.8396 - acc: 0.7518 - val_loss: 0.8162 - val_acc: 0.7562 Epoch 00021: val_loss improved from 0.84178 to 0.81622, saving model to weights-improvement-best-model.hdf5 Epoch 22/500 - 3s - loss: 0.8224 - acc: 0.7516 - val_loss: 0.8027 - val_acc: 0.7585 Epoch 00022: val_loss improved from 0.81622 to 0.80274, saving model to weights-improvement-best-model.hdf5 Epoch 23/500 - 3s - loss: 0.8131 - acc: 0.7523 - val_loss: 0.7937 - val_acc: 0.7590 Epoch 00023: val_loss improved from 0.80274 to 0.79369, saving model to weights-improvement-best-model.hdf5 Epoch 24/500 - 3s - loss: 0.7932 - acc: 0.7559 - val_loss: 0.8188 - val_acc: 0.7354 Epoch 00024: val_loss did not improve from 0.79369 Epoch 25/500 - 3s - loss: 0.8081 - acc: 0.7449 - val_loss: 0.7729 - val_acc: 0.7623 Epoch 00025: val_loss improved from 0.79369 to 0.77289, saving model to weights-improvement-best-model.hdf5 Epoch 26/500 - 3s - loss: 0.7819 - acc: 0.7557 - val_loss: 0.7881 - val_acc: 0.7508 Epoch 00026: val_loss did not improve from 0.77289 Epoch 27/500 - 3s - loss: 0.7836 - acc: 0.7515 - val_loss: 0.7656 - val_acc: 0.7580 Epoch 00027: val_loss improved from 0.77289 to 0.76560, saving model to weights-improvement-best-model.hdf5 Epoch 28/500 - 3s - loss: 0.7769 - acc: 0.7535 - val_loss: 0.7625 - val_acc: 0.7604 Epoch 00028: val_loss improved from 0.76560 to 0.76251, saving model to weights-improvement-best-model.hdf5 Epoch 29/500 - 3s - loss: 0.7651 - acc: 0.7575 - val_loss: 0.7527 - val_acc: 0.7612 Epoch 00029: val_loss improved from 0.76251 to 0.75272, saving model to weights-improvement-best-model.hdf5 Epoch 30/500 - 3s - loss: 0.7598 - acc: 0.7575 - val_loss: 0.7561 - val_acc: 0.7574 Epoch 00030: val_loss did not improve from 0.75272 Epoch 31/500 - 3s - loss: 0.7617 - acc: 0.7561 - val_loss: 0.7713 - val_acc: 0.7491 Epoch 00031: val_loss did not improve from 0.75272 Epoch 32/500 - 3s - loss: 0.7586 - acc: 0.7558 - val_loss: 0.7516 - val_acc: 0.7549 Epoch 00032: val_loss improved from 0.75272 to 0.75158, saving model to weights-improvement-best-model.hdf5 Epoch 33/500 - 3s - loss: 0.7562 - acc: 0.7537 - val_loss: 0.7378 - val_acc: 0.7626 Epoch 00033: val_loss improved from 0.75158 to 0.73781, saving model to weights-improvement-best-model.hdf5 Epoch 34/500 - 3s - loss: 0.7405 - acc: 0.7623 - val_loss: 0.7502 - val_acc: 0.7514 Epoch 00034: val_loss did not improve from 0.73781 Epoch 35/500 - 3s - loss: 0.7572 - acc: 0.7518 - val_loss: 0.7467 - val_acc: 0.7549 Epoch 00035: val_loss did not improve from 0.73781 Epoch 36/500 - 3s - loss: 0.7422 - acc: 0.7574 - val_loss: 0.7278 - val_acc: 0.7643 Epoch 00036: val_loss improved from 0.73781 to 0.72777, saving model to weights-improvement-best-model.hdf5 Epoch 37/500 - 3s - loss: 0.7360 - acc: 0.7604 - val_loss: 0.7287 - val_acc: 0.7656 Epoch 00037: val_loss did not improve from 0.72777 Epoch 38/500 - 3s - loss: 0.7403 - acc: 0.7559 - val_loss: 0.7483 - val_acc: 0.7540 Epoch 00038: val_loss did not improve from 0.72777 Epoch 39/500 - 3s - loss: 0.7394 - acc: 0.7574 - val_loss: 0.7192 - val_acc: 0.7671 Epoch 00039: val_loss improved from 0.72777 to 0.71924, saving model to weights-improvement-best-model.hdf5 Epoch 40/500 - 3s - loss: 0.7258 - acc: 0.7639 - val_loss: 0.7197 - val_acc: 0.7661 Epoch 00040: val_loss did not improve from 0.71924 Epoch 41/500 - 3s - loss: 0.7228 - acc: 0.7638 - val_loss: 0.7359 - val_acc: 0.7582 Epoch 00041: val_loss did not improve from 0.71924 Epoch 42/500 - 3s - loss: 0.7417 - acc: 0.7508 - val_loss: 0.7197 - val_acc: 0.7644 Epoch 00042: val_loss did not improve from 0.71924 Epoch 43/500 - 3s - loss: 0.7216 - acc: 0.7629 - val_loss: 0.7261 - val_acc: 0.7610 Epoch 00043: val_loss did not improve from 0.71924 Epoch 44/500 - 3s - loss: 0.7321 - acc: 0.7571 - val_loss: 0.7144 - val_acc: 0.7654 Epoch 00044: val_loss improved from 0.71924 to 0.71436, saving model to weights-improvement-best-model.hdf5 Epoch 45/500 - 3s - loss: 0.7186 - acc: 0.7620 - val_loss: 0.7169 - val_acc: 0.7623 Epoch 00045: val_loss did not improve from 0.71436 Epoch 46/500 - 3s - loss: 0.7146 - acc: 0.7663 - val_loss: 0.7409 - val_acc: 0.7549 Epoch 00046: val_loss did not improve from 0.71436 Epoch 47/500 - 3s - loss: 0.7354 - acc: 0.7546 - val_loss: 0.7122 - val_acc: 0.7658 Epoch 00047: val_loss improved from 0.71436 to 0.71222, saving model to weights-improvement-best-model.hdf5 Epoch 48/500 - 3s - loss: 0.7100 - acc: 0.7670 - val_loss: 0.7059 - val_acc: 0.7673 Epoch 00048: val_loss improved from 0.71222 to 0.70593, saving model to weights-improvement-best-model.hdf5 Epoch 49/500 - 3s - loss: 0.7074 - acc: 0.7677 - val_loss: 0.7077 - val_acc: 0.7667 Epoch 00049: val_loss did not improve from 0.70593 Epoch 50/500 - 3s - loss: 0.7340 - acc: 0.7513 - val_loss: 0.7132 - val_acc: 0.7638 Epoch 00050: val_loss did not improve from 0.70593 Epoch 51/500 - 3s - loss: 0.7072 - acc: 0.7645 - val_loss: 0.7063 - val_acc: 0.7684 Epoch 00051: val_loss did not improve from 0.70593 Epoch 52/500 - 3s - loss: 0.7034 - acc: 0.7674 - val_loss: 0.7070 - val_acc: 0.7678 Epoch 00052: val_loss did not improve from 0.70593 Epoch 53/500 - 3s - loss: 0.7190 - acc: 0.7561 - val_loss: 0.7100 - val_acc: 0.7617 Epoch 00053: val_loss did not improve from 0.70593 Epoch 54/500 - 3s - loss: 0.7038 - acc: 0.7668 - val_loss: 0.7050 - val_acc: 0.7644 Epoch 00054: val_loss improved from 0.70593 to 0.70497, saving model to weights-improvement-best-model.hdf5 Epoch 55/500 - 3s - loss: 0.7101 - acc: 0.7632 - val_loss: 0.7032 - val_acc: 0.7681 Epoch 00055: val_loss improved from 0.70497 to 0.70317, saving model to weights-improvement-best-model.hdf5 Epoch 56/500 - 3s - loss: 0.7024 - acc: 0.7671 - val_loss: 0.7085 - val_acc: 0.7606 Epoch 00056: val_loss did not improve from 0.70317 Epoch 57/500 - 3s - loss: 0.7047 - acc: 0.7660 - val_loss: 0.7089 - val_acc: 0.7636 Epoch 00057: val_loss did not improve from 0.70317 Epoch 58/500 - 3s - loss: 0.7013 - acc: 0.7673 - val_loss: 0.7550 - val_acc: 0.7251 Epoch 00058: val_loss did not improve from 0.70317 Epoch 59/500 - 3s - loss: 0.7196 - acc: 0.7563 - val_loss: 0.7014 - val_acc: 0.7687 Epoch 00059: val_loss improved from 0.70317 to 0.70138, saving model to weights-improvement-best-model.hdf5 Epoch 60/500 - 3s - loss: 0.6953 - acc: 0.7710 - val_loss: 0.6997 - val_acc: 0.7684 Epoch 00060: val_loss improved from 0.70138 to 0.69966, saving model to weights-improvement-best-model.hdf5 Epoch 61/500 - 3s - loss: 0.6936 - acc: 0.7703 - val_loss: 0.7254 - val_acc: 0.7497 Epoch 00061: val_loss did not improve from 0.69966 Epoch 62/500 - 3s - loss: 0.7219 - acc: 0.7545 - val_loss: 0.7068 - val_acc: 0.7646 Epoch 00062: val_loss did not improve from 0.69966 Epoch 63/500 - 3s - loss: 0.6949 - acc: 0.7694 - val_loss: 0.6936 - val_acc: 0.7716 Epoch 00063: val_loss improved from 0.69966 to 0.69362, saving model to weights-improvement-best-model.hdf5 Epoch 64/500 - 3s - loss: 0.6897 - acc: 0.7712 - val_loss: 0.7014 - val_acc: 0.7650 Epoch 00064: val_loss did not improve from 0.69362 Epoch 65/500 - 3s - loss: 0.7130 - acc: 0.7574 - val_loss: 0.6948 - val_acc: 0.7692 Epoch 00065: val_loss did not improve from 0.69362 Epoch 66/500 - 3s - loss: 0.6884 - acc: 0.7716 - val_loss: 0.6958 - val_acc: 0.7652 Epoch 00066: val_loss did not improve from 0.69362 Epoch 67/500 - 3s - loss: 0.6937 - acc: 0.7693 - val_loss: 0.7088 - val_acc: 0.7604 Epoch 00067: val_loss did not improve from 0.69362 Epoch 68/500 - 3s - loss: 0.7003 - acc: 0.7623 - val_loss: 0.6924 - val_acc: 0.7652 Epoch 00068: val_loss improved from 0.69362 to 0.69243, saving model to weights-improvement-best-model.hdf5 Epoch 69/500 - 3s - loss: 0.6903 - acc: 0.7702 - val_loss: 0.6992 - val_acc: 0.7652 Epoch 00069: val_loss did not improve from 0.69243 Epoch 70/500 - 3s - loss: 0.6929 - acc: 0.7680 - val_loss: 0.6939 - val_acc: 0.7714 Epoch 00070: val_loss did not improve from 0.69243 Epoch 71/500 - 3s - loss: 0.6879 - acc: 0.7725 - val_loss: 0.7020 - val_acc: 0.7657 Epoch 00071: val_loss did not improve from 0.69243 Epoch 72/500 - 3s - loss: 0.6853 - acc: 0.7742 - val_loss: 0.7177 - val_acc: 0.7623 Epoch 00072: val_loss did not improve from 0.69243 Epoch 73/500 - 3s - loss: 0.7134 - acc: 0.7582 - val_loss: 0.6913 - val_acc: 0.7685 Epoch 00073: val_loss improved from 0.69243 to 0.69134, saving model to weights-improvement-best-model.hdf5 Epoch 74/500 - 3s - loss: 0.6820 - acc: 0.7763 - val_loss: 0.6881 - val_acc: 0.7704 Epoch 00074: val_loss improved from 0.69134 to 0.68813, saving model to weights-improvement-best-model.hdf5 Epoch 75/500 - 3s - loss: 0.6792 - acc: 0.7772 - val_loss: 0.6908 - val_acc: 0.7720 Epoch 00075: val_loss did not improve from 0.68813 Epoch 76/500 - 3s - loss: 0.6810 - acc: 0.7783 - val_loss: 0.7487 - val_acc: 0.7525 Epoch 00076: val_loss did not improve from 0.68813 Epoch 77/500 - 3s - loss: 0.7158 - acc: 0.7579 - val_loss: 0.6872 - val_acc: 0.7716 Epoch 00077: val_loss improved from 0.68813 to 0.68725, saving model to weights-improvement-best-model.hdf5 Epoch 78/500 - 3s - loss: 0.6772 - acc: 0.7780 - val_loss: 0.6892 - val_acc: 0.7705 Epoch 00078: val_loss did not improve from 0.68725 Epoch 79/500 - 3s - loss: 0.6857 - acc: 0.7737 - val_loss: 0.7127 - val_acc: 0.7586 Epoch 00079: val_loss did not improve from 0.68725 Epoch 80/500 - 3s - loss: 0.6923 - acc: 0.7701 - val_loss: 0.6839 - val_acc: 0.7699 Epoch 00080: val_loss improved from 0.68725 to 0.68388, saving model to weights-improvement-best-model.hdf5 Epoch 81/500 - 3s - loss: 0.6785 - acc: 0.7771 - val_loss: 0.6947 - val_acc: 0.7650 Epoch 00081: val_loss did not improve from 0.68388 Epoch 82/500 - 3s - loss: 0.6827 - acc: 0.7761 - val_loss: 0.7019 - val_acc: 0.7670 Epoch 00082: val_loss did not improve from 0.68388 Epoch 83/500 - 3s - loss: 0.6888 - acc: 0.7734 - val_loss: 0.7127 - val_acc: 0.7509 Epoch 00083: val_loss did not improve from 0.68388 Epoch 84/500 - 3s - loss: 0.6878 - acc: 0.7714 - val_loss: 0.6932 - val_acc: 0.7743 Epoch 00084: val_loss did not improve from 0.68388 Epoch 85/500 - 3s - loss: 0.6845 - acc: 0.7749 - val_loss: 0.6936 - val_acc: 0.7704 Epoch 00085: val_loss did not improve from 0.68388 Epoch 86/500 - 3s - loss: 0.6791 - acc: 0.7781 - val_loss: 0.6901 - val_acc: 0.7724 Epoch 00086: val_loss did not improve from 0.68388 Epoch 87/500 - 3s - loss: 0.6756 - acc: 0.7814 - val_loss: 0.6958 - val_acc: 0.7677 Epoch 00087: val_loss did not improve from 0.68388 Epoch 88/500 - 3s - loss: 0.6811 - acc: 0.7771 - val_loss: 0.7309 - val_acc: 0.7433 Epoch 00088: val_loss did not improve from 0.68388 Epoch 89/500 - 3s - loss: 0.6937 - acc: 0.7696 - val_loss: 0.6822 - val_acc: 0.7772 Epoch 00089: val_loss improved from 0.68388 to 0.68222, saving model to weights-improvement-best-model.hdf5 Epoch 90/500 - 3s - loss: 0.6707 - acc: 0.7828 - val_loss: 0.7141 - val_acc: 0.7549 Epoch 00090: val_loss did not improve from 0.68222 Epoch 91/500 - 3s - loss: 0.6863 - acc: 0.7747 - val_loss: 0.7110 - val_acc: 0.7629 Epoch 00091: val_loss did not improve from 0.68222 Epoch 92/500 - 3s - loss: 0.6825 - acc: 0.7767 - val_loss: 0.6983 - val_acc: 0.7611 Epoch 00092: val_loss did not improve from 0.68222 Epoch 93/500 - 3s - loss: 0.6767 - acc: 0.7769 - val_loss: 0.6908 - val_acc: 0.7718 Epoch 00093: val_loss did not improve from 0.68222 Epoch 94/500 - 3s - loss: 0.6771 - acc: 0.7793 - val_loss: 0.7015 - val_acc: 0.7626 Epoch 00094: val_loss did not improve from 0.68222 Epoch 95/500 - 3s - loss: 0.6831 - acc: 0.7748 - val_loss: 0.7190 - val_acc: 0.7599 Epoch 00095: val_loss did not improve from 0.68222 Epoch 96/500 - 3s - loss: 0.6805 - acc: 0.7784 - val_loss: 0.6826 - val_acc: 0.7768 Epoch 00096: val_loss did not improve from 0.68222 Epoch 97/500 - 3s - loss: 0.6650 - acc: 0.7862 - val_loss: 0.6810 - val_acc: 0.7749 Epoch 00097: val_loss improved from 0.68222 to 0.68103, saving model to weights-improvement-best-model.hdf5 Epoch 98/500 - 3s - loss: 0.6780 - acc: 0.7790 - val_loss: 0.6922 - val_acc: 0.7720 Epoch 00098: val_loss did not improve from 0.68103 Epoch 99/500 - 3s - loss: 0.6731 - acc: 0.7817 - val_loss: 0.6856 - val_acc: 0.7739 Epoch 00099: val_loss did not improve from 0.68103 Epoch 100/500 - 3s - loss: 0.6656 - acc: 0.7860 - val_loss: 0.6900 - val_acc: 0.7704 Epoch 00100: val_loss did not improve from 0.68103 Epoch 101/500 - 3s - loss: 0.6793 - acc: 0.7772 - val_loss: 0.7150 - val_acc: 0.7583 Epoch 00101: val_loss did not improve from 0.68103 Epoch 102/500 - 3s - loss: 0.6792 - acc: 0.7776 - val_loss: 0.6802 - val_acc: 0.7798 Epoch 00102: val_loss improved from 0.68103 to 0.68016, saving model to weights-improvement-best-model.hdf5 Epoch 103/500 - 3s - loss: 0.6629 - acc: 0.7861 - val_loss: 0.6899 - val_acc: 0.7721 Epoch 00103: val_loss did not improve from 0.68016 Epoch 104/500 - 3s - loss: 0.6773 - acc: 0.7778 - val_loss: 0.6950 - val_acc: 0.7672 Epoch 00104: val_loss did not improve from 0.68016 Epoch 105/500 - 3s - loss: 0.6660 - acc: 0.7839 - val_loss: 0.6814 - val_acc: 0.7789 Epoch 00105: val_loss did not improve from 0.68016 Epoch 106/500 - 3s - loss: 0.6630 - acc: 0.7865 - val_loss: 0.6790 - val_acc: 0.7784 Epoch 00106: val_loss improved from 0.68016 to 0.67900, saving model to weights-improvement-best-model.hdf5 Epoch 107/500 - 3s - loss: 0.6786 - acc: 0.7768 - val_loss: 0.7279 - val_acc: 0.7592 Epoch 00107: val_loss did not improve from 0.67900 Epoch 108/500 - 3s - loss: 0.6789 - acc: 0.7795 - val_loss: 0.6912 - val_acc: 0.7709 Epoch 00108: val_loss did not improve from 0.67900 Epoch 109/500 - 3s - loss: 0.6684 - acc: 0.7835 - val_loss: 0.6802 - val_acc: 0.7786 Epoch 00109: val_loss did not improve from 0.67900 Epoch 110/500 - 3s - loss: 0.6699 - acc: 0.7825 - val_loss: 0.7052 - val_acc: 0.7574 Epoch 00110: val_loss did not improve from 0.67900 Epoch 111/500 - 3s - loss: 0.6739 - acc: 0.7802 - val_loss: 0.6870 - val_acc: 0.7722 Epoch 00111: val_loss did not improve from 0.67900 Epoch 112/500 - 3s - loss: 0.6636 - acc: 0.7839 - val_loss: 0.7160 - val_acc: 0.7544 Epoch 00112: val_loss did not improve from 0.67900 Epoch 113/500 - 3s - loss: 0.6774 - acc: 0.7785 - val_loss: 0.6849 - val_acc: 0.7762 Epoch 00113: val_loss did not improve from 0.67900 Epoch 114/500 - 3s - loss: 0.6630 - acc: 0.7874 - val_loss: 0.6919 - val_acc: 0.7709 Epoch 00114: val_loss did not improve from 0.67900 Epoch 115/500 - 3s - loss: 0.6835 - acc: 0.7730 - val_loss: 0.6951 - val_acc: 0.7668 Epoch 00115: val_loss did not improve from 0.67900 Epoch 116/500 - 3s - loss: 0.6653 - acc: 0.7856 - val_loss: 0.7036 - val_acc: 0.7705 Epoch 00116: val_loss did not improve from 0.67900 Epoch 117/500 - 3s - loss: 0.6746 - acc: 0.7817 - val_loss: 0.6810 - val_acc: 0.7736 Epoch 00117: val_loss did not improve from 0.67900 Epoch 118/500 - 3s - loss: 0.6599 - acc: 0.7891 - val_loss: 0.6785 - val_acc: 0.7760 Epoch 00118: val_loss improved from 0.67900 to 0.67849, saving model to weights-improvement-best-model.hdf5 Epoch 119/500 - 3s - loss: 0.6555 - acc: 0.7893 - val_loss: 0.6850 - val_acc: 0.7742 Epoch 00119: val_loss did not improve from 0.67849 Epoch 120/500 - 3s - loss: 0.6691 - acc: 0.7813 - val_loss: 0.7019 - val_acc: 0.7657 Epoch 00120: val_loss did not improve from 0.67849 Epoch 121/500 - 3s - loss: 0.6687 - acc: 0.7835 - val_loss: 0.6833 - val_acc: 0.7761 Epoch 00121: val_loss did not improve from 0.67849 Epoch 122/500 - 3s - loss: 0.6616 - acc: 0.7843 - val_loss: 0.6879 - val_acc: 0.7702 Epoch 00122: val_loss did not improve from 0.67849 Epoch 123/500 - 3s - loss: 0.6608 - acc: 0.7856 - val_loss: 0.6904 - val_acc: 0.7755 Epoch 00123: val_loss did not improve from 0.67849 Epoch 124/500 - 3s - loss: 0.6629 - acc: 0.7842 - val_loss: 0.6777 - val_acc: 0.7764 Epoch 00124: val_loss improved from 0.67849 to 0.67774, saving model to weights-improvement-best-model.hdf5 Epoch 125/500 - 3s - loss: 0.6525 - acc: 0.7931 - val_loss: 0.6864 - val_acc: 0.7721 Epoch 00125: val_loss did not improve from 0.67774 Epoch 126/500 - 3s - loss: 0.6651 - acc: 0.7860 - val_loss: 0.7114 - val_acc: 0.7530 Epoch 00126: val_loss did not improve from 0.67774 Epoch 127/500 - 3s - loss: 0.6716 - acc: 0.7809 - val_loss: 0.6854 - val_acc: 0.7754 Epoch 00127: val_loss did not improve from 0.67774 Epoch 128/500 - 3s - loss: 0.6568 - acc: 0.7911 - val_loss: 0.6940 - val_acc: 0.7716 Epoch 00128: val_loss did not improve from 0.67774 Epoch 129/500 - 3s - loss: 0.6747 - acc: 0.7783 - val_loss: 0.7097 - val_acc: 0.7554 Epoch 00129: val_loss did not improve from 0.67774 Epoch 130/500 - 3s - loss: 0.6652 - acc: 0.7840 - val_loss: 0.6791 - val_acc: 0.7755 Epoch 00130: val_loss did not improve from 0.67774 Epoch 131/500 - 3s - loss: 0.6514 - acc: 0.7933 - val_loss: 0.6773 - val_acc: 0.7744 Epoch 00131: val_loss improved from 0.67774 to 0.67731, saving model to weights-improvement-best-model.hdf5 Epoch 132/500 - 3s - loss: 0.6538 - acc: 0.7920 - val_loss: 0.7346 - val_acc: 0.7592 Epoch 00132: val_loss did not improve from 0.67731 Epoch 133/500 - 3s - loss: 0.6856 - acc: 0.7750 - val_loss: 0.6769 - val_acc: 0.7790 Epoch 00133: val_loss improved from 0.67731 to 0.67688, saving model to weights-improvement-best-model.hdf5 Epoch 134/500 - 3s - loss: 0.6515 - acc: 0.7967 - val_loss: 0.6827 - val_acc: 0.7721 Epoch 00134: val_loss did not improve from 0.67688 Epoch 135/500 - 3s - loss: 0.6523 - acc: 0.7919 - val_loss: 0.6906 - val_acc: 0.7775 Epoch 00135: val_loss did not improve from 0.67688 Epoch 136/500 - 3s - loss: 0.6708 - acc: 0.7819 - val_loss: 0.6772 - val_acc: 0.7829 Epoch 00136: val_loss did not improve from 0.67688 Epoch 137/500 - 3s - loss: 0.6500 - acc: 0.7942 - val_loss: 0.6771 - val_acc: 0.7801 Epoch 00137: val_loss did not improve from 0.67688 Epoch 138/500 - 3s - loss: 0.6649 - acc: 0.7851 - val_loss: 0.7093 - val_acc: 0.7650 Epoch 00138: val_loss did not improve from 0.67688 Epoch 139/500 - 3s - loss: 0.6624 - acc: 0.7871 - val_loss: 0.7079 - val_acc: 0.7557 Epoch 00139: val_loss did not improve from 0.67688 Epoch 140/500 - 3s - loss: 0.6692 - acc: 0.7821 - val_loss: 0.6798 - val_acc: 0.7812 Epoch 00140: val_loss did not improve from 0.67688 Epoch 141/500 - 3s - loss: 0.6498 - acc: 0.7931 - val_loss: 0.6884 - val_acc: 0.7658 Epoch 00141: val_loss did not improve from 0.67688 Epoch 142/500 - 3s - loss: 0.6656 - acc: 0.7857 - val_loss: 0.6868 - val_acc: 0.7759 Epoch 00142: val_loss did not improve from 0.67688 Epoch 143/500 - 3s - loss: 0.6505 - acc: 0.7945 - val_loss: 0.6839 - val_acc: 0.7757 Epoch 00143: val_loss did not improve from 0.67688 Epoch 144/500 - 3s - loss: 0.6675 - acc: 0.7836 - val_loss: 0.7076 - val_acc: 0.7573 Epoch 00144: val_loss did not improve from 0.67688 Epoch 145/500 - 3s - loss: 0.6593 - acc: 0.7897 - val_loss: 0.6774 - val_acc: 0.7789 Epoch 00145: val_loss did not improve from 0.67688 Epoch 146/500 - 3s - loss: 0.6468 - acc: 0.7950 - val_loss: 0.6780 - val_acc: 0.7791 Epoch 00146: val_loss did not improve from 0.67688 Epoch 147/500 - 3s - loss: 0.6634 - acc: 0.7837 - val_loss: 0.6771 - val_acc: 0.7813 Epoch 00147: val_loss did not improve from 0.67688 Epoch 148/500 - 3s - loss: 0.6493 - acc: 0.7943 - val_loss: 0.6780 - val_acc: 0.7741 Epoch 00148: val_loss did not improve from 0.67688 Epoch 149/500 - 3s - loss: 0.6494 - acc: 0.7937 - val_loss: 0.7406 - val_acc: 0.7612 Epoch 00149: val_loss did not improve from 0.67688 Epoch 150/500 - 3s - loss: 0.6723 - acc: 0.7845 - val_loss: 0.7060 - val_acc: 0.7583 Epoch 00150: val_loss did not improve from 0.67688 Epoch 151/500 - 3s - loss: 0.6636 - acc: 0.7864 - val_loss: 0.6923 - val_acc: 0.7734 Epoch 00151: val_loss did not improve from 0.67688 Epoch 152/500 - 3s - loss: 0.6513 - acc: 0.7932 - val_loss: 0.6851 - val_acc: 0.7780 Epoch 00152: val_loss did not improve from 0.67688 Epoch 153/500 - 3s - loss: 0.6462 - acc: 0.7978 - val_loss: 0.7064 - val_acc: 0.7710 Epoch 00153: val_loss did not improve from 0.67688 Epoch 154/500 - 3s - loss: 0.6728 - acc: 0.7813 - val_loss: 0.6810 - val_acc: 0.7738 Epoch 00154: val_loss did not improve from 0.67688 Epoch 155/500 - 3s - loss: 0.6481 - acc: 0.7971 - val_loss: 0.6882 - val_acc: 0.7763 Epoch 00155: val_loss did not improve from 0.67688 Epoch 156/500 - 3s - loss: 0.6634 - acc: 0.7867 - val_loss: 0.6849 - val_acc: 0.7791 Epoch 00156: val_loss did not improve from 0.67688 Epoch 157/500 - 3s - loss: 0.6493 - acc: 0.7973 - val_loss: 0.6844 - val_acc: 0.7731 Epoch 00157: val_loss did not improve from 0.67688 Epoch 158/500 - 3s - loss: 0.6522 - acc: 0.7899 - val_loss: 0.7029 - val_acc: 0.7607 Epoch 00158: val_loss did not improve from 0.67688 Epoch 159/500 - 3s - loss: 0.6599 - acc: 0.7882 - val_loss: 0.6785 - val_acc: 0.7790 Epoch 00159: val_loss did not improve from 0.67688 Epoch 160/500 - 3s - loss: 0.6462 - acc: 0.7977 - val_loss: 0.6825 - val_acc: 0.7723 Epoch 00160: val_loss did not improve from 0.67688 Epoch 161/500 - 3s - loss: 0.6649 - acc: 0.7843 - val_loss: 0.6854 - val_acc: 0.7728 Epoch 00161: val_loss did not improve from 0.67688 Epoch 162/500 - 3s - loss: 0.6450 - acc: 0.7991 - val_loss: 0.6782 - val_acc: 0.7799 Epoch 00162: val_loss did not improve from 0.67688 Epoch 163/500 - 3s - loss: 0.6427 - acc: 0.7987 - val_loss: 0.6958 - val_acc: 0.7723 Epoch 00163: val_loss did not improve from 0.67688 Epoch 164/500 - 3s - loss: 0.6795 - acc: 0.7772 - val_loss: 0.6712 - val_acc: 0.7795 Epoch 00164: val_loss improved from 0.67688 to 0.67119, saving model to weights-improvement-best-model.hdf5 Epoch 165/500 - 3s - loss: 0.6432 - acc: 0.7982 - val_loss: 0.6875 - val_acc: 0.7766 Epoch 00165: val_loss did not improve from 0.67119 Epoch 166/500 - 3s - loss: 0.6545 - acc: 0.7907 - val_loss: 0.6796 - val_acc: 0.7761 Epoch 00166: val_loss did not improve from 0.67119 Epoch 167/500 - 3s - loss: 0.6486 - acc: 0.7933 - val_loss: 0.6914 - val_acc: 0.7774 Epoch 00167: val_loss did not improve from 0.67119 Epoch 168/500 - 3s - loss: 0.6538 - acc: 0.7910 - val_loss: 0.6865 - val_acc: 0.7775 Epoch 00168: val_loss did not improve from 0.67119 Epoch 169/500 - 3s - loss: 0.6519 - acc: 0.7932 - val_loss: 0.6824 - val_acc: 0.7784 Epoch 00169: val_loss did not improve from 0.67119 Epoch 170/500 - 3s - loss: 0.6493 - acc: 0.7928 - val_loss: 0.6925 - val_acc: 0.7634 Epoch 00170: val_loss did not improve from 0.67119 Epoch 171/500 - 3s - loss: 0.6542 - acc: 0.7902 - val_loss: 0.7087 - val_acc: 0.7704 Epoch 00171: val_loss did not improve from 0.67119 Epoch 172/500 - 3s - loss: 0.6526 - acc: 0.7928 - val_loss: 0.7007 - val_acc: 0.7625 Epoch 00172: val_loss did not improve from 0.67119 Epoch 173/500 - 3s - loss: 0.6572 - acc: 0.7876 - val_loss: 0.6781 - val_acc: 0.7817 Epoch 00173: val_loss did not improve from 0.67119 Epoch 174/500 - 3s - loss: 0.6379 - acc: 0.8015 - val_loss: 0.6865 - val_acc: 0.7734 Epoch 00174: val_loss did not improve from 0.67119 Epoch 175/500 - 3s - loss: 0.6559 - acc: 0.7883 - val_loss: 0.6737 - val_acc: 0.7817 Epoch 00175: val_loss did not improve from 0.67119 Epoch 176/500 - 3s - loss: 0.6401 - acc: 0.7994 - val_loss: 0.6948 - val_acc: 0.7723 Epoch 00176: val_loss did not improve from 0.67119 Epoch 177/500 - 3s - loss: 0.6610 - acc: 0.7871 - val_loss: 0.6923 - val_acc: 0.7702 Epoch 00177: val_loss did not improve from 0.67119 Epoch 178/500 - 3s - loss: 0.6484 - acc: 0.7958 - val_loss: 0.6840 - val_acc: 0.7750 Epoch 00178: val_loss did not improve from 0.67119 Epoch 179/500 - 3s - loss: 0.6412 - acc: 0.7987 - val_loss: 0.6823 - val_acc: 0.7803 Epoch 00179: val_loss did not improve from 0.67119 Epoch 180/500 - 3s - loss: 0.6623 - acc: 0.7862 - val_loss: 0.6829 - val_acc: 0.7787 Epoch 00180: val_loss did not improve from 0.67119 Epoch 181/500 - 3s - loss: 0.6425 - acc: 0.7966 - val_loss: 0.6835 - val_acc: 0.7815 Epoch 00181: val_loss did not improve from 0.67119 Epoch 182/500 - 3s - loss: 0.6441 - acc: 0.7950 - val_loss: 0.6781 - val_acc: 0.7791 Epoch 00182: val_loss did not improve from 0.67119 Epoch 183/500 - 3s - loss: 0.6432 - acc: 0.7968 - val_loss: 0.6849 - val_acc: 0.7743 Epoch 00183: val_loss did not improve from 0.67119 Epoch 184/500 - 3s - loss: 0.6473 - acc: 0.7957 - val_loss: 0.7240 - val_acc: 0.7671 Epoch 00184: val_loss did not improve from 0.67119 Epoch 185/500 - 3s - loss: 0.6598 - acc: 0.7882 - val_loss: 0.6770 - val_acc: 0.7766 Epoch 00185: val_loss did not improve from 0.67119 Epoch 186/500 - 3s - loss: 0.6465 - acc: 0.7959 - val_loss: 0.6883 - val_acc: 0.7743 Epoch 00186: val_loss did not improve from 0.67119 Epoch 187/500 - 3s - loss: 0.6418 - acc: 0.7977 - val_loss: 0.6901 - val_acc: 0.7703 Epoch 00187: val_loss did not improve from 0.67119 Epoch 188/500 - 3s - loss: 0.6435 - acc: 0.7954 - val_loss: 0.6928 - val_acc: 0.7748 Epoch 00188: val_loss did not improve from 0.67119 Epoch 189/500 - 3s - loss: 0.6528 - acc: 0.7930 - val_loss: 0.6787 - val_acc: 0.7758 Epoch 00189: val_loss did not improve from 0.67119 Epoch 190/500 - 3s - loss: 0.6374 - acc: 0.8012 - val_loss: 0.6826 - val_acc: 0.7786 Epoch 00190: val_loss did not improve from 0.67119 Epoch 191/500 - 3s - loss: 0.6359 - acc: 0.8029 - val_loss: 0.6848 - val_acc: 0.7824 Epoch 00191: val_loss did not improve from 0.67119 Epoch 192/500 - 3s - loss: 0.6391 - acc: 0.8022 - val_loss: 0.6773 - val_acc: 0.7780 Epoch 00192: val_loss did not improve from 0.67119 Epoch 193/500 - 3s - loss: 0.6759 - acc: 0.7783 - val_loss: 0.6853 - val_acc: 0.7742 Epoch 00193: val_loss did not improve from 0.67119 Epoch 194/500 - 3s - loss: 0.6390 - acc: 0.7996 - val_loss: 0.6795 - val_acc: 0.7770 Epoch 00194: val_loss did not improve from 0.67119 Epoch 195/500 - 3s - loss: 0.6400 - acc: 0.7979 - val_loss: 0.7035 - val_acc: 0.7732 Epoch 00195: val_loss did not improve from 0.67119 Epoch 196/500 - 3s - loss: 0.6561 - acc: 0.7898 - val_loss: 0.6977 - val_acc: 0.7666 Epoch 00196: val_loss did not improve from 0.67119 Epoch 197/500 - 3s - loss: 0.6433 - acc: 0.7958 - val_loss: 0.6798 - val_acc: 0.7774 Epoch 00197: val_loss did not improve from 0.67119 Epoch 198/500 - 3s - loss: 0.6379 - acc: 0.8005 - val_loss: 0.6847 - val_acc: 0.7749 Epoch 00198: val_loss did not improve from 0.67119 Epoch 199/500 - 3s - loss: 0.6481 - acc: 0.7922 - val_loss: 0.6840 - val_acc: 0.7742 Epoch 00199: val_loss did not improve from 0.67119 Epoch 200/500 - 3s - loss: 0.6344 - acc: 0.8012 - val_loss: 0.6901 - val_acc: 0.7754 Epoch 00200: val_loss did not improve from 0.67119 Epoch 201/500 - 3s - loss: 0.6584 - acc: 0.7887 - val_loss: 0.6753 - val_acc: 0.7830 Epoch 00201: val_loss did not improve from 0.67119 Epoch 202/500 - 3s - loss: 0.6371 - acc: 0.8003 - val_loss: 0.6886 - val_acc: 0.7780 Epoch 00202: val_loss did not improve from 0.67119 Epoch 203/500 - 3s - loss: 0.6528 - acc: 0.7920 - val_loss: 0.6736 - val_acc: 0.7846 Epoch 00203: val_loss did not improve from 0.67119 Epoch 204/500 - 3s - loss: 0.6341 - acc: 0.8045 - val_loss: 0.6985 - val_acc: 0.7691 Epoch 00204: val_loss did not improve from 0.67119 Epoch 205/500 - 3s - loss: 0.6527 - acc: 0.7925 - val_loss: 0.6900 - val_acc: 0.7646 Epoch 00205: val_loss did not improve from 0.67119 Epoch 206/500 - 3s - loss: 0.6498 - acc: 0.7896 - val_loss: 0.6947 - val_acc: 0.7769 Epoch 00206: val_loss did not improve from 0.67119 Epoch 207/500 - 3s - loss: 0.6412 - acc: 0.7997 - val_loss: 0.6727 - val_acc: 0.7785 Epoch 00207: val_loss did not improve from 0.67119 Epoch 208/500 - 3s - loss: 0.6320 - acc: 0.8061 - val_loss: 0.6858 - val_acc: 0.7755 Epoch 00208: val_loss did not improve from 0.67119 Epoch 209/500 - 3s - loss: 0.6339 - acc: 0.8027 - val_loss: 0.7185 - val_acc: 0.7692 Epoch 00209: val_loss did not improve from 0.67119 Epoch 210/500 - 3s - loss: 0.6715 - acc: 0.7808 - val_loss: 0.6726 - val_acc: 0.7825 Epoch 00210: val_loss did not improve from 0.67119 Epoch 211/500 - 3s - loss: 0.6307 - acc: 0.8045 - val_loss: 0.6795 - val_acc: 0.7839 Epoch 00211: val_loss did not improve from 0.67119 Epoch 212/500 - 3s - loss: 0.6393 - acc: 0.7986 - val_loss: 0.6887 - val_acc: 0.7732 Epoch 00212: val_loss did not improve from 0.67119 Epoch 213/500 - 3s - loss: 0.6496 - acc: 0.7947 - val_loss: 0.6737 - val_acc: 0.7828 Epoch 00213: val_loss did not improve from 0.67119 Epoch 214/500 - 3s - loss: 0.6324 - acc: 0.8044 - val_loss: 0.6696 - val_acc: 0.7800 Epoch 00214: val_loss improved from 0.67119 to 0.66956, saving model to weights-improvement-best-model.hdf5 Epoch 215/500 - 3s - loss: 0.6309 - acc: 0.8052 - val_loss: 0.6847 - val_acc: 0.7713 Epoch 00215: val_loss did not improve from 0.66956 Epoch 216/500 - 3s - loss: 0.6629 - acc: 0.7870 - val_loss: 0.7237 - val_acc: 0.7683 Epoch 00216: val_loss did not improve from 0.66956 Epoch 217/500 - 3s - loss: 0.6469 - acc: 0.7980 - val_loss: 0.6757 - val_acc: 0.7812 Epoch 00217: val_loss did not improve from 0.66956 Epoch 218/500 - 3s - loss: 0.6331 - acc: 0.8029 - val_loss: 0.6745 - val_acc: 0.7773 Epoch 00218: val_loss did not improve from 0.66956 Epoch 219/500 - 3s - loss: 0.6398 - acc: 0.7994 - val_loss: 0.6802 - val_acc: 0.7830 Epoch 00219: val_loss did not improve from 0.66956 Epoch 220/500 - 3s - loss: 0.6373 - acc: 0.8005 - val_loss: 0.7087 - val_acc: 0.7517 Epoch 00220: val_loss did not improve from 0.66956 Epoch 221/500 - 3s - loss: 0.6527 - acc: 0.7910 - val_loss: 0.6850 - val_acc: 0.7782 Epoch 00221: val_loss did not improve from 0.66956 Epoch 222/500 - 3s - loss: 0.6330 - acc: 0.8040 - val_loss: 0.6838 - val_acc: 0.7778 Epoch 00222: val_loss did not improve from 0.66956 Epoch 223/500 - 3s - loss: 0.6431 - acc: 0.7980 - val_loss: 0.6815 - val_acc: 0.7749 Epoch 00223: val_loss did not improve from 0.66956 Epoch 224/500 - 3s - loss: 0.6356 - acc: 0.8027 - val_loss: 0.6942 - val_acc: 0.7746 Epoch 00224: val_loss did not improve from 0.66956 Epoch 225/500 - 3s - loss: 0.6486 - acc: 0.7935 - val_loss: 0.6912 - val_acc: 0.7612 Epoch 00225: val_loss did not improve from 0.66956 Epoch 226/500 - 3s - loss: 0.6381 - acc: 0.7990 - val_loss: 0.6918 - val_acc: 0.7740 Epoch 00226: val_loss did not improve from 0.66956 Epoch 227/500 - 3s - loss: 0.6380 - acc: 0.8000 - val_loss: 0.6832 - val_acc: 0.7763 Epoch 00227: val_loss did not improve from 0.66956 Epoch 228/500 - 3s - loss: 0.6398 - acc: 0.7980 - val_loss: 0.6830 - val_acc: 0.7787 Epoch 00228: val_loss did not improve from 0.66956 Epoch 229/500 - 3s - loss: 0.6371 - acc: 0.7985 - val_loss: 0.6814 - val_acc: 0.7706 Epoch 00229: val_loss did not improve from 0.66956 Epoch 230/500 - 4s - loss: 0.6426 - acc: 0.7985 - val_loss: 0.6818 - val_acc: 0.7795 Epoch 00230: val_loss did not improve from 0.66956 Epoch 231/500 - 3s - loss: 0.6313 - acc: 0.8060 - val_loss: 0.6734 - val_acc: 0.7811 Epoch 00231: val_loss did not improve from 0.66956 Epoch 232/500 - 3s - loss: 0.6471 - acc: 0.7957 - val_loss: 0.6893 - val_acc: 0.7703 Epoch 00232: val_loss did not improve from 0.66956 Epoch 233/500 - 3s - loss: 0.6312 - acc: 0.8064 - val_loss: 0.6817 - val_acc: 0.7825 Epoch 00233: val_loss did not improve from 0.66956 Epoch 234/500 - 3s - loss: 0.6348 - acc: 0.8044 - val_loss: 0.6956 - val_acc: 0.7667 Epoch 00234: val_loss did not improve from 0.66956 Epoch 235/500 - 3s - loss: 0.6473 - acc: 0.7962 - val_loss: 0.6958 - val_acc: 0.7786 Epoch 00235: val_loss did not improve from 0.66956 Epoch 236/500 - 3s - loss: 0.6464 - acc: 0.7957 - val_loss: 0.6835 - val_acc: 0.7741 Epoch 00236: val_loss did not improve from 0.66956 Epoch 237/500 - 3s - loss: 0.6328 - acc: 0.8031 - val_loss: 0.6922 - val_acc: 0.7757 Epoch 00237: val_loss did not improve from 0.66956 Epoch 238/500 - 3s - loss: 0.6342 - acc: 0.8057 - val_loss: 0.6855 - val_acc: 0.7733 Epoch 00238: val_loss did not improve from 0.66956 Epoch 239/500 - 3s - loss: 0.6476 - acc: 0.7928 - val_loss: 0.6780 - val_acc: 0.7787 Epoch 00239: val_loss did not improve from 0.66956 Epoch 240/500 - 3s - loss: 0.6270 - acc: 0.8062 - val_loss: 0.6869 - val_acc: 0.7771 Epoch 00240: val_loss did not improve from 0.66956 Epoch 241/500 - 3s - loss: 0.6420 - acc: 0.7965 - val_loss: 0.7095 - val_acc: 0.7703 Epoch 00241: val_loss did not improve from 0.66956 Epoch 242/500 - 3s - loss: 0.6385 - acc: 0.8021 - val_loss: 0.6807 - val_acc: 0.7795 Epoch 00242: val_loss did not improve from 0.66956 Epoch 243/500 - 3s - loss: 0.6284 - acc: 0.8052 - val_loss: 0.6933 - val_acc: 0.7769 Epoch 00243: val_loss did not improve from 0.66956 Epoch 244/500 - 3s - loss: 0.6435 - acc: 0.7970 - val_loss: 0.6946 - val_acc: 0.7629 Epoch 00244: val_loss did not improve from 0.66956 Epoch 245/500 - 3s - loss: 0.6373 - acc: 0.8019 - val_loss: 0.6802 - val_acc: 0.7775 Epoch 00245: val_loss did not improve from 0.66956 Epoch 246/500 - 3s - loss: 0.6367 - acc: 0.8014 - val_loss: 0.6936 - val_acc: 0.7672 Epoch 00246: val_loss did not improve from 0.66956 Epoch 247/500 - 3s - loss: 0.6322 - acc: 0.8046 - val_loss: 0.6822 - val_acc: 0.7829 Epoch 00247: val_loss did not improve from 0.66956 Epoch 248/500 - 3s - loss: 0.6353 - acc: 0.8016 - val_loss: 0.6898 - val_acc: 0.7714 Epoch 00248: val_loss did not improve from 0.66956 Epoch 249/500 - 3s - loss: 0.6358 - acc: 0.8003 - val_loss: 0.6774 - val_acc: 0.7807 Epoch 00249: val_loss did not improve from 0.66956 Epoch 250/500 - 3s - loss: 0.6274 - acc: 0.8043 - val_loss: 0.6746 - val_acc: 0.7827 Epoch 00250: val_loss did not improve from 0.66956 Epoch 251/500 - 3s - loss: 0.6212 - acc: 0.8077 - val_loss: 0.6931 - val_acc: 0.7666 Epoch 00251: val_loss did not improve from 0.66956 Epoch 252/500 - 3s - loss: 0.6608 - acc: 0.7881 - val_loss: 0.6938 - val_acc: 0.7782 Epoch 00252: val_loss did not improve from 0.66956 Epoch 253/500 - 3s - loss: 0.6291 - acc: 0.8054 - val_loss: 0.6769 - val_acc: 0.7780 Epoch 00253: val_loss did not improve from 0.66956 Epoch 254/500 - 3s - loss: 0.6247 - acc: 0.8090 - val_loss: 0.6919 - val_acc: 0.7751 Epoch 00254: val_loss did not improve from 0.66956 Epoch 255/500 - 3s - loss: 0.6511 - acc: 0.7934 - val_loss: 0.6776 - val_acc: 0.7811 Epoch 00255: val_loss did not improve from 0.66956 Epoch 256/500 - 3s - loss: 0.6258 - acc: 0.8091 - val_loss: 0.6738 - val_acc: 0.7856 Epoch 00256: val_loss did not improve from 0.66956 Epoch 257/500 - 3s - loss: 0.6291 - acc: 0.8046 - val_loss: 0.6796 - val_acc: 0.7726 Epoch 00257: val_loss did not improve from 0.66956 Epoch 258/500 - 3s - loss: 0.6341 - acc: 0.8031 - val_loss: 0.7047 - val_acc: 0.7720 Epoch 00258: val_loss did not improve from 0.66956 Epoch 259/500 - 3s - loss: 0.6365 - acc: 0.7978 - val_loss: 0.6968 - val_acc: 0.7699 Epoch 00259: val_loss did not improve from 0.66956 Epoch 260/500 - 3s - loss: 0.6332 - acc: 0.8014 - val_loss: 0.6914 - val_acc: 0.7809 Epoch 00260: val_loss did not improve from 0.66956 Epoch 261/500 - 3s - loss: 0.6311 - acc: 0.8036 - val_loss: 0.6966 - val_acc: 0.7666 Epoch 00261: val_loss did not improve from 0.66956 Epoch 262/500 - 3s - loss: 0.6433 - acc: 0.7961 - val_loss: 0.6844 - val_acc: 0.7772 Epoch 00262: val_loss did not improve from 0.66956 Epoch 263/500 - 3s - loss: 0.6322 - acc: 0.8026 - val_loss: 0.6807 - val_acc: 0.7733 Epoch 00263: val_loss did not improve from 0.66956 Epoch 264/500 - 3s - loss: 0.6253 - acc: 0.8061 - val_loss: 0.6793 - val_acc: 0.7819 Epoch 00264: val_loss did not improve from 0.66956 Epoch 265/500 - 3s - loss: 0.6256 - acc: 0.8046 - val_loss: 0.6865 - val_acc: 0.7714 Epoch 00265: val_loss did not improve from 0.66956 Epoch 266/500 - 3s - loss: 0.6446 - acc: 0.7948 - val_loss: 0.6989 - val_acc: 0.7806 Epoch 00266: val_loss did not improve from 0.66956 Epoch 267/500 - 3s - loss: 0.6340 - acc: 0.8024 - val_loss: 0.7139 - val_acc: 0.7542 Epoch 00267: val_loss did not improve from 0.66956 Epoch 268/500 - 3s - loss: 0.6397 - acc: 0.7963 - val_loss: 0.6818 - val_acc: 0.7818 Epoch 00268: val_loss did not improve from 0.66956 Epoch 269/500 - 3s - loss: 0.6217 - acc: 0.8103 - val_loss: 0.6819 - val_acc: 0.7731 Epoch 00269: val_loss did not improve from 0.66956 Epoch 270/500 - 3s - loss: 0.6358 - acc: 0.8017 - val_loss: 0.6972 - val_acc: 0.7760 Epoch 00270: val_loss did not improve from 0.66956 Epoch 271/500 - 3s - loss: 0.6331 - acc: 0.8019 - val_loss: 0.6740 - val_acc: 0.7803 Epoch 00271: val_loss did not improve from 0.66956 Epoch 272/500 - 3s - loss: 0.6245 - acc: 0.8063 - val_loss: 0.7027 - val_acc: 0.7748 Epoch 00272: val_loss did not improve from 0.66956 Epoch 273/500 - 3s - loss: 0.6384 - acc: 0.7999 - val_loss: 0.7039 - val_acc: 0.7594 Epoch 00273: val_loss did not improve from 0.66956 Epoch 274/500 - 3s - loss: 0.6358 - acc: 0.8019 - val_loss: 0.6735 - val_acc: 0.7827 Epoch 00274: val_loss did not improve from 0.66956 Epoch 275/500 - 3s - loss: 0.6201 - acc: 0.8105 - val_loss: 0.6733 - val_acc: 0.7796 Epoch 00275: val_loss did not improve from 0.66956 Epoch 276/500 - 3s - loss: 0.6191 - acc: 0.8096 - val_loss: 0.6817 - val_acc: 0.7748 Epoch 00276: val_loss did not improve from 0.66956 Epoch 277/500 - 3s - loss: 0.6536 - acc: 0.7886 - val_loss: 0.6813 - val_acc: 0.7788 Epoch 00277: val_loss did not improve from 0.66956 Epoch 278/500 - 3s - loss: 0.6207 - acc: 0.8099 - val_loss: 0.7231 - val_acc: 0.7496 Epoch 00278: val_loss did not improve from 0.66956 Epoch 279/500 - 3s - loss: 0.6488 - acc: 0.7931 - val_loss: 0.6830 - val_acc: 0.7787 Epoch 00279: val_loss did not improve from 0.66956 Epoch 280/500 - 3s - loss: 0.6200 - acc: 0.8104 - val_loss: 0.6757 - val_acc: 0.7800 Epoch 00280: val_loss did not improve from 0.66956 Epoch 281/500 - 3s - loss: 0.6222 - acc: 0.8085 - val_loss: 0.6863 - val_acc: 0.7802 Epoch 00281: val_loss did not improve from 0.66956 Epoch 282/500 - 3s - loss: 0.6317 - acc: 0.8035 - val_loss: 0.6997 - val_acc: 0.7647 Epoch 00282: val_loss did not improve from 0.66956 Epoch 283/500 - 3s - loss: 0.6402 - acc: 0.7973 - val_loss: 0.6855 - val_acc: 0.7780 Epoch 00283: val_loss did not improve from 0.66956 Epoch 284/500 - 3s - loss: 0.6289 - acc: 0.8035 - val_loss: 0.6759 - val_acc: 0.7794 Epoch 00284: val_loss did not improve from 0.66956 Epoch 285/500 - 3s - loss: 0.6162 - acc: 0.8112 - val_loss: 0.6923 - val_acc: 0.7751 Epoch 00285: val_loss did not improve from 0.66956 Epoch 286/500 - 3s - loss: 0.6372 - acc: 0.8019 - val_loss: 0.6984 - val_acc: 0.7736 Epoch 00286: val_loss did not improve from 0.66956 Epoch 287/500 - 4s - loss: 0.6325 - acc: 0.8041 - val_loss: 0.6838 - val_acc: 0.7795 Epoch 00287: val_loss did not improve from 0.66956 Epoch 288/500 - 3s - loss: 0.6240 - acc: 0.8057 - val_loss: 0.6725 - val_acc: 0.7821 Epoch 00288: val_loss did not improve from 0.66956 Epoch 289/500 - 3s - loss: 0.6176 - acc: 0.8098 - val_loss: 0.6818 - val_acc: 0.7766 Epoch 00289: val_loss did not improve from 0.66956 Epoch 290/500 - 3s - loss: 0.6371 - acc: 0.7991 - val_loss: 0.6899 - val_acc: 0.7812 Epoch 00290: val_loss did not improve from 0.66956 Epoch 291/500 - 3s - loss: 0.6291 - acc: 0.8068 - val_loss: 0.6904 - val_acc: 0.7630 Epoch 00291: val_loss did not improve from 0.66956 Epoch 292/500 - 3s - loss: 0.6370 - acc: 0.7988 - val_loss: 0.6798 - val_acc: 0.7796 Epoch 00292: val_loss did not improve from 0.66956 Epoch 293/500 - 3s - loss: 0.6160 - acc: 0.8113 - val_loss: 0.6998 - val_acc: 0.7765 Epoch 00293: val_loss did not improve from 0.66956 Epoch 294/500 - 3s - loss: 0.6298 - acc: 0.8026 - val_loss: 0.6956 - val_acc: 0.7763 Epoch 00294: val_loss did not improve from 0.66956 Epoch 295/500 - 3s - loss: 0.6369 - acc: 0.7990 - val_loss: 0.6986 - val_acc: 0.7607 Epoch 00295: val_loss did not improve from 0.66956 Epoch 296/500 - 3s - loss: 0.6262 - acc: 0.8040 - val_loss: 0.6705 - val_acc: 0.7830 Epoch 00296: val_loss did not improve from 0.66956 Epoch 297/500 - 3s - loss: 0.6151 - acc: 0.8133 - val_loss: 0.6820 - val_acc: 0.7747 Epoch 00297: val_loss did not improve from 0.66956 Epoch 298/500 - 3s - loss: 0.6324 - acc: 0.8023 - val_loss: 0.6956 - val_acc: 0.7622 Epoch 00298: val_loss did not improve from 0.66956 Epoch 299/500 - 3s - loss: 0.6321 - acc: 0.8038 - val_loss: 0.6755 - val_acc: 0.7846 Epoch 00299: val_loss did not improve from 0.66956 Epoch 300/500 - 3s - loss: 0.6250 - acc: 0.8062 - val_loss: 0.6771 - val_acc: 0.7779 Epoch 00300: val_loss did not improve from 0.66956 Epoch 301/500 - 3s - loss: 0.6222 - acc: 0.8070 - val_loss: 0.6792 - val_acc: 0.7800 Epoch 00301: val_loss did not improve from 0.66956 Epoch 302/500 - 3s - loss: 0.6200 - acc: 0.8070 - val_loss: 0.6897 - val_acc: 0.7723 Epoch 00302: val_loss did not improve from 0.66956 Epoch 303/500 - 3s - loss: 0.6200 - acc: 0.8100 - val_loss: 0.6866 - val_acc: 0.7794 Epoch 00303: val_loss did not improve from 0.66956 Epoch 304/500 - 3s - loss: 0.6413 - acc: 0.7956 - val_loss: 0.6727 - val_acc: 0.7816 Epoch 00304: val_loss did not improve from 0.66956 Epoch 305/500 - 3s - loss: 0.6157 - acc: 0.8109 - val_loss: 0.6769 - val_acc: 0.7853 Epoch 00305: val_loss did not improve from 0.66956 Epoch 306/500 - 3s - loss: 0.6276 - acc: 0.8034 - val_loss: 0.7038 - val_acc: 0.7697 Epoch 00306: val_loss did not improve from 0.66956 Epoch 307/500 - 3s - loss: 0.6290 - acc: 0.8033 - val_loss: 0.6837 - val_acc: 0.7830 Epoch 00307: val_loss did not improve from 0.66956 Epoch 308/500 - 3s - loss: 0.6161 - acc: 0.8107 - val_loss: 0.6838 - val_acc: 0.7782 Epoch 00308: val_loss did not improve from 0.66956 Epoch 309/500 - 3s - loss: 0.6232 - acc: 0.8062 - val_loss: 0.6922 - val_acc: 0.7777 Epoch 00309: val_loss did not improve from 0.66956 Epoch 310/500 - 3s - loss: 0.6287 - acc: 0.8044 - val_loss: 0.6848 - val_acc: 0.7769 Epoch 00310: val_loss did not improve from 0.66956 Epoch 311/500 - 3s - loss: 0.6229 - acc: 0.8106 - val_loss: 0.6869 - val_acc: 0.7797 Epoch 00311: val_loss did not improve from 0.66956 Epoch 312/500 - 3s - loss: 0.6250 - acc: 0.8069 - val_loss: 0.6858 - val_acc: 0.7746 Epoch 00312: val_loss did not improve from 0.66956 Epoch 313/500 - 3s - loss: 0.6269 - acc: 0.8054 - val_loss: 0.7099 - val_acc: 0.7734 Epoch 00313: val_loss did not improve from 0.66956 Epoch 314/500 - 3s - loss: 0.6353 - acc: 0.8024 - val_loss: 0.6855 - val_acc: 0.7742 Epoch 00314: val_loss did not improve from 0.66956 Epoch 315/500 - 3s - loss: 0.6209 - acc: 0.8097 - val_loss: 0.6911 - val_acc: 0.7805 Epoch 00315: val_loss did not improve from 0.66956 Epoch 316/500 - 3s - loss: 0.6224 - acc: 0.8091 - val_loss: 0.7003 - val_acc: 0.7658 Epoch 00316: val_loss did not improve from 0.66956 Epoch 317/500 - 3s - loss: 0.6378 - acc: 0.7986 - val_loss: 0.6826 - val_acc: 0.7824 Epoch 00317: val_loss did not improve from 0.66956 Epoch 318/500 - 3s - loss: 0.6156 - acc: 0.8147 - val_loss: 0.6748 - val_acc: 0.7833 Epoch 00318: val_loss did not improve from 0.66956 Epoch 319/500 - 3s - loss: 0.6186 - acc: 0.8090 - val_loss: 0.7245 - val_acc: 0.7701 Epoch 00319: val_loss did not improve from 0.66956 Epoch 320/500 - 3s - loss: 0.6412 - acc: 0.7977 - val_loss: 0.6718 - val_acc: 0.7828 Epoch 00320: val_loss did not improve from 0.66956 Epoch 321/500 - 3s - loss: 0.6133 - acc: 0.8136 - val_loss: 0.6947 - val_acc: 0.7858 Epoch 00321: val_loss did not improve from 0.66956 Epoch 322/500 - 3s - loss: 0.6242 - acc: 0.8077 - val_loss: 0.6928 - val_acc: 0.7716 Epoch 00322: val_loss did not improve from 0.66956 Epoch 323/500 - 3s - loss: 0.6327 - acc: 0.8021 - val_loss: 0.6760 - val_acc: 0.7788 Epoch 00323: val_loss did not improve from 0.66956 Epoch 324/500 - 3s - loss: 0.6144 - acc: 0.8145 - val_loss: 0.7220 - val_acc: 0.7778 Epoch 00324: val_loss did not improve from 0.66956 Epoch 325/500 - 3s - loss: 0.6310 - acc: 0.8048 - val_loss: 0.6808 - val_acc: 0.7741 Epoch 00325: val_loss did not improve from 0.66956 Epoch 326/500 - 3s - loss: 0.6218 - acc: 0.8074 - val_loss: 0.7095 - val_acc: 0.7757 Epoch 00326: val_loss did not improve from 0.66956 Epoch 327/500 - 3s - loss: 0.6301 - acc: 0.8044 - val_loss: 0.6728 - val_acc: 0.7814 Epoch 00327: val_loss did not improve from 0.66956 Epoch 328/500 - 3s - loss: 0.6147 - acc: 0.8122 - val_loss: 0.6847 - val_acc: 0.7829 Epoch 00328: val_loss did not improve from 0.66956 Epoch 329/500 - 3s - loss: 0.6254 - acc: 0.8091 - val_loss: 0.6806 - val_acc: 0.7738 Epoch 00329: val_loss did not improve from 0.66956 Epoch 330/500 - 3s - loss: 0.6232 - acc: 0.8080 - val_loss: 0.7037 - val_acc: 0.7768 Epoch 00330: val_loss did not improve from 0.66956 Epoch 331/500 - 3s - loss: 0.6248 - acc: 0.8074 - val_loss: 0.7197 - val_acc: 0.7563 Epoch 00331: val_loss did not improve from 0.66956 Epoch 332/500 - 3s - loss: 0.6277 - acc: 0.8043 - val_loss: 0.6817 - val_acc: 0.7796 Epoch 00332: val_loss did not improve from 0.66956 Epoch 333/500 - 3s - loss: 0.6147 - acc: 0.8154 - val_loss: 0.6929 - val_acc: 0.7656 Epoch 00333: val_loss did not improve from 0.66956 Epoch 334/500 - 3s - loss: 0.6359 - acc: 0.7978 - val_loss: 0.6888 - val_acc: 0.7804 Epoch 00334: val_loss did not improve from 0.66956 Epoch 335/500 - 3s - loss: 0.6177 - acc: 0.8105 - val_loss: 0.6795 - val_acc: 0.7782 Epoch 00335: val_loss did not improve from 0.66956 Epoch 336/500 - 3s - loss: 0.6127 - acc: 0.8142 - val_loss: 0.6866 - val_acc: 0.7779 Epoch 00336: val_loss did not improve from 0.66956 Epoch 337/500 - 3s - loss: 0.6114 - acc: 0.8154 - val_loss: 0.6868 - val_acc: 0.7788 Epoch 00337: val_loss did not improve from 0.66956 Epoch 338/500 - 3s - loss: 0.6335 - acc: 0.8016 - val_loss: 0.6754 - val_acc: 0.7840 Epoch 00338: val_loss did not improve from 0.66956 Epoch 339/500 - 3s - loss: 0.6112 - acc: 0.8151 - val_loss: 0.7180 - val_acc: 0.7721 Epoch 00339: val_loss did not improve from 0.66956 Epoch 340/500 - 3s - loss: 0.6313 - acc: 0.8028 - val_loss: 0.6779 - val_acc: 0.7786 Epoch 00340: val_loss did not improve from 0.66956 Epoch 341/500 - 3s - loss: 0.6113 - acc: 0.8116 - val_loss: 0.6905 - val_acc: 0.7799 Epoch 00341: val_loss did not improve from 0.66956 Epoch 342/500 - 3s - loss: 0.6190 - acc: 0.8088 - val_loss: 0.7001 - val_acc: 0.7614 Epoch 00342: val_loss did not improve from 0.66956 Epoch 343/500 - 3s - loss: 0.6224 - acc: 0.8069 - val_loss: 0.6720 - val_acc: 0.7876 Epoch 00343: val_loss did not improve from 0.66956 Epoch 344/500 - 3s - loss: 0.6091 - acc: 0.8159 - val_loss: 0.6828 - val_acc: 0.7814 Epoch 00344: val_loss did not improve from 0.66956 Epoch 345/500 - 3s - loss: 0.6228 - acc: 0.8077 - val_loss: 0.6997 - val_acc: 0.7592 Epoch 00345: val_loss did not improve from 0.66956 Epoch 346/500 - 3s - loss: 0.6226 - acc: 0.8068 - val_loss: 0.6754 - val_acc: 0.7841 Epoch 00346: val_loss did not improve from 0.66956 Epoch 347/500 - 3s - loss: 0.6079 - acc: 0.8148 - val_loss: 0.6992 - val_acc: 0.7683 Epoch 00347: val_loss did not improve from 0.66956 Epoch 348/500 - 3s - loss: 0.6383 - acc: 0.7973 - val_loss: 0.6826 - val_acc: 0.7844 Epoch 00348: val_loss did not improve from 0.66956 Epoch 349/500 - 3s - loss: 0.6099 - acc: 0.8151 - val_loss: 0.6729 - val_acc: 0.7849 Epoch 00349: val_loss did not improve from 0.66956 Epoch 350/500 - 3s - loss: 0.6285 - acc: 0.8038 - val_loss: 0.6803 - val_acc: 0.7835 Epoch 00350: val_loss did not improve from 0.66956 Epoch 351/500 - 3s - loss: 0.6067 - acc: 0.8172 - val_loss: 0.6963 - val_acc: 0.7813 Epoch 00351: val_loss did not improve from 0.66956 Epoch 352/500 - 3s - loss: 0.6166 - acc: 0.8138 - val_loss: 0.6897 - val_acc: 0.7740 Epoch 00352: val_loss did not improve from 0.66956 Epoch 353/500 - 4s - loss: 0.6420 - acc: 0.7946 - val_loss: 0.6964 - val_acc: 0.7768 Epoch 00353: val_loss did not improve from 0.66956 Epoch 354/500 - 3s - loss: 0.6182 - acc: 0.8095 - val_loss: 0.6828 - val_acc: 0.7742 Epoch 00354: val_loss did not improve from 0.66956 Epoch 355/500 - 4s - loss: 0.6180 - acc: 0.8090 - val_loss: 0.6728 - val_acc: 0.7876 Epoch 00355: val_loss did not improve from 0.66956 Epoch 356/500 - 3s - loss: 0.6123 - acc: 0.8141 - val_loss: 0.7266 - val_acc: 0.7536 Epoch 00356: val_loss did not improve from 0.66956 Epoch 357/500 - 3s - loss: 0.6327 - acc: 0.8016 - val_loss: 0.6785 - val_acc: 0.7823 Epoch 00357: val_loss did not improve from 0.66956 Epoch 358/500 - 3s - loss: 0.6072 - acc: 0.8159 - val_loss: 0.6885 - val_acc: 0.7806 Epoch 00358: val_loss did not improve from 0.66956 Epoch 359/500 - 3s - loss: 0.6229 - acc: 0.8082 - val_loss: 0.6938 - val_acc: 0.7653 Epoch 00359: val_loss did not improve from 0.66956 Epoch 360/500 - 3s - loss: 0.6205 - acc: 0.8073 - val_loss: 0.6875 - val_acc: 0.7829 Epoch 00360: val_loss did not improve from 0.66956 Epoch 361/500 - 3s - loss: 0.6166 - acc: 0.8129 - val_loss: 0.6966 - val_acc: 0.7735 Epoch 00361: val_loss did not improve from 0.66956 Epoch 362/500 - 3s - loss: 0.6244 - acc: 0.8065 - val_loss: 0.6856 - val_acc: 0.7776 Epoch 00362: val_loss did not improve from 0.66956 Epoch 363/500 - 3s - loss: 0.6166 - acc: 0.8098 - val_loss: 0.7199 - val_acc: 0.7512 Epoch 00363: val_loss did not improve from 0.66956 Epoch 364/500 - 3s - loss: 0.6274 - acc: 0.8040 - val_loss: 0.6790 - val_acc: 0.7870 Epoch 00364: val_loss did not improve from 0.66956 Epoch 365/500 - 3s - loss: 0.6091 - acc: 0.8160 - val_loss: 0.6744 - val_acc: 0.7788 Epoch 00365: val_loss did not improve from 0.66956 Epoch 366/500 - 3s - loss: 0.6229 - acc: 0.8059 - val_loss: 0.6974 - val_acc: 0.7805 Epoch 00366: val_loss did not improve from 0.66956 Epoch 367/500 - 3s - loss: 0.6139 - acc: 0.8123 - val_loss: 0.6802 - val_acc: 0.7837 Epoch 00367: val_loss did not improve from 0.66956 Epoch 368/500 - 3s - loss: 0.6170 - acc: 0.8109 - val_loss: 0.6954 - val_acc: 0.7827 Epoch 00368: val_loss did not improve from 0.66956 Epoch 369/500 - 3s - loss: 0.6224 - acc: 0.8082 - val_loss: 0.6846 - val_acc: 0.7704 Epoch 00369: val_loss did not improve from 0.66956 Epoch 370/500 - 3s - loss: 0.6184 - acc: 0.8098 - val_loss: 0.6834 - val_acc: 0.7845 Epoch 00370: val_loss did not improve from 0.66956 Epoch 371/500 - 3s - loss: 0.6148 - acc: 0.8127 - val_loss: 0.7027 - val_acc: 0.7627 Epoch 00371: val_loss did not improve from 0.66956 Epoch 372/500 - 3s - loss: 0.6243 - acc: 0.8090 - val_loss: 0.6754 - val_acc: 0.7879 Epoch 00372: val_loss did not improve from 0.66956 Epoch 373/500 - 3s - loss: 0.6046 - acc: 0.8178 - val_loss: 0.6973 - val_acc: 0.7770 Epoch 00373: val_loss did not improve from 0.66956 Epoch 374/500 - 3s - loss: 0.6325 - acc: 0.8012 - val_loss: 0.6921 - val_acc: 0.7693 Epoch 00374: val_loss did not improve from 0.66956 Epoch 375/500 - 3s - loss: 0.6140 - acc: 0.8131 - val_loss: 0.6838 - val_acc: 0.7821 Epoch 00375: val_loss did not improve from 0.66956 Epoch 376/500 - 3s - loss: 0.6114 - acc: 0.8143 - val_loss: 0.6828 - val_acc: 0.7851 Epoch 00376: val_loss did not improve from 0.66956 Epoch 377/500 - 3s - loss: 0.6132 - acc: 0.8131 - val_loss: 0.6777 - val_acc: 0.7825 Epoch 00377: val_loss did not improve from 0.66956 Epoch 378/500 - 3s - loss: 0.6131 - acc: 0.8141 - val_loss: 0.6975 - val_acc: 0.7768 Epoch 00378: val_loss did not improve from 0.66956 Epoch 379/500 - 3s - loss: 0.6225 - acc: 0.8066 - val_loss: 0.6951 - val_acc: 0.7701 Epoch 00379: val_loss did not improve from 0.66956 Epoch 380/500 - 3s - loss: 0.6195 - acc: 0.8084 - val_loss: 0.6755 - val_acc: 0.7841 Epoch 00380: val_loss did not improve from 0.66956 Epoch 381/500 - 3s - loss: 0.6061 - acc: 0.8181 - val_loss: 0.6804 - val_acc: 0.7770 Epoch 00381: val_loss did not improve from 0.66956 Epoch 382/500 - 3s - loss: 0.6165 - acc: 0.8097 - val_loss: 0.7139 - val_acc: 0.7728 Epoch 00382: val_loss did not improve from 0.66956 Epoch 383/500 - 3s - loss: 0.6263 - acc: 0.8045 - val_loss: 0.6773 - val_acc: 0.7839 Epoch 00383: val_loss did not improve from 0.66956 Epoch 384/500 - 3s - loss: 0.6040 - acc: 0.8186 - val_loss: 0.7015 - val_acc: 0.7698 Epoch 00384: val_loss did not improve from 0.66956 Epoch 385/500 - 3s - loss: 0.6329 - acc: 0.7998 - val_loss: 0.6737 - val_acc: 0.7894 Epoch 00385: val_loss did not improve from 0.66956 Epoch 386/500 - 3s - loss: 0.6080 - acc: 0.8154 - val_loss: 0.6927 - val_acc: 0.7717 Epoch 00386: val_loss did not improve from 0.66956 Epoch 387/500 - 3s - loss: 0.6238 - acc: 0.8060 - val_loss: 0.6859 - val_acc: 0.7833 Epoch 00387: val_loss did not improve from 0.66956 Epoch 388/500 - 3s - loss: 0.6087 - acc: 0.8138 - val_loss: 0.6822 - val_acc: 0.7742 Epoch 00388: val_loss did not improve from 0.66956 Epoch 389/500 - 3s - loss: 0.6163 - acc: 0.8089 - val_loss: 0.6913 - val_acc: 0.7811 Epoch 00389: val_loss did not improve from 0.66956 Epoch 390/500 - 3s - loss: 0.6118 - acc: 0.8122 - val_loss: 0.6939 - val_acc: 0.7713 Epoch 00390: val_loss did not improve from 0.66956 Epoch 391/500 - 3s - loss: 0.6272 - acc: 0.8045 - val_loss: 0.6903 - val_acc: 0.7816 Epoch 00391: val_loss did not improve from 0.66956 Epoch 392/500 - 3s - loss: 0.6090 - acc: 0.8147 - val_loss: 0.6843 - val_acc: 0.7783 Epoch 00392: val_loss did not improve from 0.66956 Epoch 393/500 - 3s - loss: 0.6049 - acc: 0.8159 - val_loss: 0.6827 - val_acc: 0.7807 Epoch 00393: val_loss did not improve from 0.66956 Epoch 394/500 - 3s - loss: 0.6230 - acc: 0.8072 - val_loss: 0.6986 - val_acc: 0.7671 Epoch 00394: val_loss did not improve from 0.66956 Epoch 395/500 - 3s - loss: 0.6164 - acc: 0.8116 - val_loss: 0.6831 - val_acc: 0.7869 Epoch 00395: val_loss did not improve from 0.66956 Epoch 396/500 - 3s - loss: 0.6080 - acc: 0.8172 - val_loss: 0.6780 - val_acc: 0.7836 Epoch 00396: val_loss did not improve from 0.66956 Epoch 397/500 - 3s - loss: 0.6033 - acc: 0.8174 - val_loss: 0.6927 - val_acc: 0.7654 Epoch 00397: val_loss did not improve from 0.66956 Epoch 398/500 - 3s - loss: 0.6430 - acc: 0.7956 - val_loss: 0.6853 - val_acc: 0.7859 Epoch 00398: val_loss did not improve from 0.66956 Epoch 399/500 - 3s - loss: 0.6064 - acc: 0.8174 - val_loss: 0.6876 - val_acc: 0.7723 Epoch 00399: val_loss did not improve from 0.66956 Epoch 400/500 - 3s - loss: 0.6186 - acc: 0.8060 - val_loss: 0.6945 - val_acc: 0.7809 Epoch 00400: val_loss did not improve from 0.66956 Epoch 401/500 - 3s - loss: 0.6148 - acc: 0.8127 - val_loss: 0.6870 - val_acc: 0.7762 Epoch 00401: val_loss did not improve from 0.66956 Epoch 402/500 - 3s - loss: 0.6156 - acc: 0.8098 - val_loss: 0.7292 - val_acc: 0.7708 Epoch 00402: val_loss did not improve from 0.66956 Epoch 403/500 - 3s - loss: 0.6235 - acc: 0.8077 - val_loss: 0.6800 - val_acc: 0.7790 Epoch 00403: val_loss did not improve from 0.66956 Epoch 404/500 - 3s - loss: 0.6034 - acc: 0.8200 - val_loss: 0.6870 - val_acc: 0.7801 Epoch 00404: val_loss did not improve from 0.66956 Epoch 405/500 - 3s - loss: 0.6031 - acc: 0.8191 - val_loss: 0.6777 - val_acc: 0.7821 Epoch 00405: val_loss did not improve from 0.66956 Epoch 406/500 - 3s - loss: 0.6048 - acc: 0.8169 - val_loss: 0.7706 - val_acc: 0.7231 Epoch 00406: val_loss did not improve from 0.66956 Epoch 407/500 - 3s - loss: 0.6424 - acc: 0.7955 - val_loss: 0.6808 - val_acc: 0.7852 Epoch 00407: val_loss did not improve from 0.66956 Epoch 408/500 - 3s - loss: 0.6002 - acc: 0.8205 - val_loss: 0.6868 - val_acc: 0.7815 Epoch 00408: val_loss did not improve from 0.66956 Epoch 409/500 - 3s - loss: 0.6067 - acc: 0.8182 - val_loss: 0.7265 - val_acc: 0.7481 Epoch 00409: val_loss did not improve from 0.66956 Epoch 410/500 - 3s - loss: 0.6344 - acc: 0.8003 - val_loss: 0.6797 - val_acc: 0.7799 Epoch 00410: val_loss did not improve from 0.66956 Epoch 411/500 - 3s - loss: 0.6025 - acc: 0.8187 - val_loss: 0.7259 - val_acc: 0.7490 Epoch 00411: val_loss did not improve from 0.66956 Epoch 412/500 - 3s - loss: 0.6306 - acc: 0.8006 - val_loss: 0.6872 - val_acc: 0.7815 Epoch 00412: val_loss did not improve from 0.66956 Epoch 413/500 - 3s - loss: 0.6048 - acc: 0.8174 - val_loss: 0.6899 - val_acc: 0.7802 Epoch 00413: val_loss did not improve from 0.66956 Epoch 414/500 - 3s - loss: 0.6163 - acc: 0.8097 - val_loss: 0.6870 - val_acc: 0.7822 Epoch 00414: val_loss did not improve from 0.66956 Epoch 415/500 - 3s - loss: 0.6119 - acc: 0.8120 - val_loss: 0.6676 - val_acc: 0.7872 Epoch 00415: val_loss improved from 0.66956 to 0.66761, saving model to weights-improvement-best-model.hdf5 Epoch 416/500 - 3s - loss: 0.6052 - acc: 0.8171 - val_loss: 0.6906 - val_acc: 0.7774 Epoch 00416: val_loss did not improve from 0.66761 Epoch 417/500 - 3s - loss: 0.6135 - acc: 0.8133 - val_loss: 0.7054 - val_acc: 0.7653 Epoch 00417: val_loss did not improve from 0.66761 Epoch 418/500 - 3s - loss: 0.6190 - acc: 0.8096 - val_loss: 0.6728 - val_acc: 0.7849 Epoch 00418: val_loss did not improve from 0.66761 Epoch 419/500 - 3s - loss: 0.6028 - acc: 0.8203 - val_loss: 0.7077 - val_acc: 0.7666 Epoch 00419: val_loss did not improve from 0.66761 Epoch 420/500 - 3s - loss: 0.6248 - acc: 0.8042 - val_loss: 0.7219 - val_acc: 0.7775 Epoch 00420: val_loss did not improve from 0.66761 Epoch 421/500 - 3s - loss: 0.6206 - acc: 0.8118 - val_loss: 0.6797 - val_acc: 0.7825 Epoch 00421: val_loss did not improve from 0.66761 Epoch 422/500 - 3s - loss: 0.6019 - acc: 0.8189 - val_loss: 0.6961 - val_acc: 0.7827 Epoch 00422: val_loss did not improve from 0.66761 Epoch 423/500 - 3s - loss: 0.6197 - acc: 0.8097 - val_loss: 0.7074 - val_acc: 0.7636 Epoch 00423: val_loss did not improve from 0.66761 Epoch 424/500 - 3s - loss: 0.6163 - acc: 0.8114 - val_loss: 0.6816 - val_acc: 0.7768 Epoch 00424: val_loss did not improve from 0.66761 Epoch 425/500 - 3s - loss: 0.6037 - acc: 0.8181 - val_loss: 0.6910 - val_acc: 0.7820 Epoch 00425: val_loss did not improve from 0.66761 Epoch 426/500 - 3s - loss: 0.6169 - acc: 0.8111 - val_loss: 0.6842 - val_acc: 0.7723 Epoch 00426: val_loss did not improve from 0.66761 Epoch 427/500 - 3s - loss: 0.6109 - acc: 0.8139 - val_loss: 0.6749 - val_acc: 0.7792 Epoch 00427: val_loss did not improve from 0.66761 Epoch 428/500 - 3s - loss: 0.6024 - acc: 0.8217 - val_loss: 0.6964 - val_acc: 0.7743 Epoch 00428: val_loss did not improve from 0.66761 Epoch 429/500 - 3s - loss: 0.6190 - acc: 0.8088 - val_loss: 0.6916 - val_acc: 0.7692 Epoch 00429: val_loss did not improve from 0.66761 Epoch 430/500 - 3s - loss: 0.6116 - acc: 0.8139 - val_loss: 0.6800 - val_acc: 0.7852 Epoch 00430: val_loss did not improve from 0.66761 Epoch 431/500 - 3s - loss: 0.6043 - acc: 0.8171 - val_loss: 0.7123 - val_acc: 0.7513 Epoch 00431: val_loss did not improve from 0.66761 Epoch 432/500 - 3s - loss: 0.6180 - acc: 0.8081 - val_loss: 0.6829 - val_acc: 0.7790 Epoch 00432: val_loss did not improve from 0.66761 Epoch 433/500 - 3s - loss: 0.6013 - acc: 0.8210 - val_loss: 0.6839 - val_acc: 0.7852 Epoch 00433: val_loss did not improve from 0.66761 Epoch 434/500 - 3s - loss: 0.6114 - acc: 0.8132 - val_loss: 0.7026 - val_acc: 0.7693 Epoch 00434: val_loss did not improve from 0.66761 Epoch 435/500 - 3s - loss: 0.6097 - acc: 0.8133 - val_loss: 0.6997 - val_acc: 0.7800 Epoch 00435: val_loss did not improve from 0.66761 Epoch 436/500 - 3s - loss: 0.6162 - acc: 0.8111 - val_loss: 0.7092 - val_acc: 0.7681 Epoch 00436: val_loss did not improve from 0.66761 Epoch 437/500 - 3s - loss: 0.6096 - acc: 0.8133 - val_loss: 0.6809 - val_acc: 0.7842 Epoch 00437: val_loss did not improve from 0.66761 Epoch 438/500 - 3s - loss: 0.6027 - acc: 0.8174 - val_loss: 0.6960 - val_acc: 0.7666 Epoch 00438: val_loss did not improve from 0.66761 Epoch 439/500 - 3s - loss: 0.6122 - acc: 0.8108 - val_loss: 0.6912 - val_acc: 0.7704 Epoch 00439: val_loss did not improve from 0.66761 Epoch 440/500 - 3s - loss: 0.6073 - acc: 0.8178 - val_loss: 0.6849 - val_acc: 0.7848 Epoch 00440: val_loss did not improve from 0.66761 Epoch 441/500 - 3s - loss: 0.6105 - acc: 0.8125 - val_loss: 0.7076 - val_acc: 0.7648 Epoch 00441: val_loss did not improve from 0.66761 Epoch 442/500 - 3s - loss: 0.6132 - acc: 0.8105 - val_loss: 0.6832 - val_acc: 0.7847 Epoch 00442: val_loss did not improve from 0.66761 Epoch 443/500 - 3s - loss: 0.5998 - acc: 0.8207 - val_loss: 0.7214 - val_acc: 0.7524 Epoch 00443: val_loss did not improve from 0.66761 Epoch 444/500 - 3s - loss: 0.6242 - acc: 0.8046 - val_loss: 0.6968 - val_acc: 0.7763 Epoch 00444: val_loss did not improve from 0.66761 Epoch 445/500 - 3s - loss: 0.6127 - acc: 0.8138 - val_loss: 0.7058 - val_acc: 0.7775 Epoch 00445: val_loss did not improve from 0.66761 Epoch 446/500 - 3s - loss: 0.6074 - acc: 0.8164 - val_loss: 0.6861 - val_acc: 0.7738 Epoch 00446: val_loss did not improve from 0.66761 Epoch 447/500 - 3s - loss: 0.6011 - acc: 0.8219 - val_loss: 0.6864 - val_acc: 0.7830 Epoch 00447: val_loss did not improve from 0.66761 Epoch 448/500 - 3s - loss: 0.6039 - acc: 0.8171 - val_loss: 0.6899 - val_acc: 0.7759 Epoch 00448: val_loss did not improve from 0.66761 Epoch 449/500 - 3s - loss: 0.6087 - acc: 0.8153 - val_loss: 0.6948 - val_acc: 0.7800 Epoch 00449: val_loss did not improve from 0.66761 Epoch 450/500 - 3s - loss: 0.6058 - acc: 0.8158 - val_loss: 0.6865 - val_acc: 0.7755 Epoch 00450: val_loss did not improve from 0.66761 Epoch 451/500 - 3s - loss: 0.6015 - acc: 0.8190 - val_loss: 0.6933 - val_acc: 0.7814 Epoch 00451: val_loss did not improve from 0.66761 Epoch 452/500 - 3s - loss: 0.6162 - acc: 0.8122 - val_loss: 0.6995 - val_acc: 0.7672 Epoch 00452: val_loss did not improve from 0.66761 Epoch 453/500 - 3s - loss: 0.6134 - acc: 0.8145 - val_loss: 0.6786 - val_acc: 0.7795 Epoch 00453: val_loss did not improve from 0.66761 Epoch 454/500 - 4s - loss: 0.5942 - acc: 0.8243 - val_loss: 0.6872 - val_acc: 0.7845 Epoch 00454: val_loss did not improve from 0.66761 Epoch 455/500 - 4s - loss: 0.6156 - acc: 0.8126 - val_loss: 0.7103 - val_acc: 0.7786 Epoch 00455: val_loss did not improve from 0.66761 Epoch 456/500 - 4s - loss: 0.6165 - acc: 0.8103 - val_loss: 0.6899 - val_acc: 0.7694 Epoch 00456: val_loss did not improve from 0.66761 Epoch 457/500 - 4s - loss: 0.6024 - acc: 0.8182 - val_loss: 0.6872 - val_acc: 0.7815 Epoch 00457: val_loss did not improve from 0.66761 Epoch 458/500 - 4s - loss: 0.6082 - acc: 0.8163 - val_loss: 0.7075 - val_acc: 0.7644 Epoch 00458: val_loss did not improve from 0.66761 Epoch 459/500 - 3s - loss: 0.6153 - acc: 0.8091 - val_loss: 0.6998 - val_acc: 0.7818 Epoch 00459: val_loss did not improve from 0.66761 Epoch 460/500 - 3s - loss: 0.6000 - acc: 0.8214 - val_loss: 0.6965 - val_acc: 0.7636 Epoch 00460: val_loss did not improve from 0.66761 Epoch 461/500 - 3s - loss: 0.6201 - acc: 0.8071 - val_loss: 0.7067 - val_acc: 0.7867 Epoch 00461: val_loss did not improve from 0.66761 Epoch 462/500 - 3s - loss: 0.6019 - acc: 0.8185 - val_loss: 0.7207 - val_acc: 0.7739 Epoch 00462: val_loss did not improve from 0.66761 Epoch 463/500 - 3s - loss: 0.6146 - acc: 0.8113 - val_loss: 0.7035 - val_acc: 0.7811 Epoch 00463: val_loss did not improve from 0.66761 Epoch 464/500 - 3s - loss: 0.6071 - acc: 0.8155 - val_loss: 0.7011 - val_acc: 0.7678 Epoch 00464: val_loss did not improve from 0.66761 Epoch 465/500 - 3s - loss: 0.6172 - acc: 0.8087 - val_loss: 0.6810 - val_acc: 0.7828 Epoch 00465: val_loss did not improve from 0.66761 Epoch 466/500 - 3s - loss: 0.5972 - acc: 0.8215 - val_loss: 0.6888 - val_acc: 0.7730 Epoch 00466: val_loss did not improve from 0.66761 Epoch 467/500 - 3s - loss: 0.6108 - acc: 0.8107 - val_loss: 0.6896 - val_acc: 0.7798 Epoch 00467: val_loss did not improve from 0.66761 Epoch 468/500 - 3s - loss: 0.5997 - acc: 0.8195 - val_loss: 0.7396 - val_acc: 0.7479 Epoch 00468: val_loss did not improve from 0.66761 Epoch 469/500 - 3s - loss: 0.6247 - acc: 0.8044 - val_loss: 0.6798 - val_acc: 0.7826 Epoch 00469: val_loss did not improve from 0.66761 Epoch 470/500 - 3s - loss: 0.5963 - acc: 0.8209 - val_loss: 0.6866 - val_acc: 0.7768 Epoch 00470: val_loss did not improve from 0.66761 Epoch 471/500 - 3s - loss: 0.6041 - acc: 0.8186 - val_loss: 0.7183 - val_acc: 0.7756 Epoch 00471: val_loss did not improve from 0.66761 Epoch 472/500 - 3s - loss: 0.6138 - acc: 0.8122 - val_loss: 0.6831 - val_acc: 0.7741 Epoch 00472: val_loss did not improve from 0.66761 Epoch 473/500 - 3s - loss: 0.5992 - acc: 0.8213 - val_loss: 0.7156 - val_acc: 0.7745 Epoch 00473: val_loss did not improve from 0.66761 Epoch 474/500 - 3s - loss: 0.6215 - acc: 0.8081 - val_loss: 0.7073 - val_acc: 0.7764 Epoch 00474: val_loss did not improve from 0.66761 Epoch 475/500 - 3s - loss: 0.6057 - acc: 0.8185 - val_loss: 0.6968 - val_acc: 0.7795 Epoch 00475: val_loss did not improve from 0.66761 Epoch 476/500 - 3s - loss: 0.5988 - acc: 0.8199 - val_loss: 0.6983 - val_acc: 0.7779 Epoch 00476: val_loss did not improve from 0.66761 Epoch 477/500 - 3s - loss: 0.5973 - acc: 0.8208 - val_loss: 0.6854 - val_acc: 0.7783 Epoch 00477: val_loss did not improve from 0.66761 Epoch 478/500 - 3s - loss: 0.6129 - acc: 0.8105 - val_loss: 0.7018 - val_acc: 0.7629 Epoch 00478: val_loss did not improve from 0.66761 Epoch 479/500 - 3s - loss: 0.6070 - acc: 0.8166 - val_loss: 0.6746 - val_acc: 0.7796 Epoch 00479: val_loss did not improve from 0.66761 Epoch 480/500 - 3s - loss: 0.5924 - acc: 0.8253 - val_loss: 0.7259 - val_acc: 0.7592 Epoch 00480: val_loss did not improve from 0.66761 Epoch 481/500 - 3s - loss: 0.6275 - acc: 0.8022 - val_loss: 0.6822 - val_acc: 0.7780 Epoch 00481: val_loss did not improve from 0.66761 Epoch 482/500 - 3s - loss: 0.5975 - acc: 0.8194 - val_loss: 0.6873 - val_acc: 0.7817 Epoch 00482: val_loss did not improve from 0.66761 Epoch 483/500 - 3s - loss: 0.6011 - acc: 0.8199 - val_loss: 0.6959 - val_acc: 0.7696 Epoch 00483: val_loss did not improve from 0.66761 Epoch 484/500 - 3s - loss: 0.6074 - acc: 0.8169 - val_loss: 0.7176 - val_acc: 0.7721 Epoch 00484: val_loss did not improve from 0.66761 Epoch 485/500 - 3s - loss: 0.6173 - acc: 0.8103 - val_loss: 0.6835 - val_acc: 0.7828 Epoch 00485: val_loss did not improve from 0.66761 Epoch 486/500 - 3s - loss: 0.5919 - acc: 0.8239 - val_loss: 0.7227 - val_acc: 0.7484 Epoch 00486: val_loss did not improve from 0.66761 Epoch 487/500 - 3s - loss: 0.6256 - acc: 0.8012 - val_loss: 0.6888 - val_acc: 0.7819 Epoch 00487: val_loss did not improve from 0.66761 Epoch 488/500 - 3s - loss: 0.6009 - acc: 0.8198 - val_loss: 0.6865 - val_acc: 0.7831 Epoch 00488: val_loss did not improve from 0.66761 Epoch 489/500 - 3s - loss: 0.5929 - acc: 0.8218 - val_loss: 0.6826 - val_acc: 0.7771 Epoch 00489: val_loss did not improve from 0.66761 Epoch 490/500 - 3s - loss: 0.6015 - acc: 0.8201 - val_loss: 0.7042 - val_acc: 0.7746 Epoch 00490: val_loss did not improve from 0.66761 Epoch 491/500 - 3s - loss: 0.6278 - acc: 0.8021 - val_loss: 0.6886 - val_acc: 0.7717 Epoch 00491: val_loss did not improve from 0.66761 Epoch 492/500 - 3s - loss: 0.5970 - acc: 0.8213 - val_loss: 0.6987 - val_acc: 0.7821 Epoch 00492: val_loss did not improve from 0.66761 Epoch 493/500 - 3s - loss: 0.6124 - acc: 0.8130 - val_loss: 0.7058 - val_acc: 0.7672 Epoch 00493: val_loss did not improve from 0.66761 Epoch 494/500 - 3s - loss: 0.6058 - acc: 0.8143 - val_loss: 0.6868 - val_acc: 0.7867 Epoch 00494: val_loss did not improve from 0.66761 Epoch 495/500 - 3s - loss: 0.5987 - acc: 0.8198 - val_loss: 0.7000 - val_acc: 0.7734 Epoch 00495: val_loss did not improve from 0.66761 Epoch 496/500 - 3s - loss: 0.6132 - acc: 0.8113 - val_loss: 0.7104 - val_acc: 0.7779 Epoch 00496: val_loss did not improve from 0.66761 Epoch 497/500 - 3s - loss: 0.6042 - acc: 0.8166 - val_loss: 0.6733 - val_acc: 0.7845 Epoch 00497: val_loss did not improve from 0.66761 Epoch 498/500 - 3s - loss: 0.5922 - acc: 0.8248 - val_loss: 0.6995 - val_acc: 0.7842 Epoch 00498: val_loss did not improve from 0.66761 Epoch 499/500 - 3s - loss: 0.6194 - acc: 0.8080 - val_loss: 0.6885 - val_acc: 0.7747 Epoch 00499: val_loss did not improve from 0.66761 Epoch 500/500 - 3s - loss: 0.6000 - acc: 0.8198 - val_loss: 0.7014 - val_acc: 0.7760 Epoch 00500: val_loss did not improve from 0.66761
# trying another architechture : model_1
model_1 = Sequential()
model_1.add(Dense(1024, kernel_regularizer=regularizers.l2(.001),input_dim=X_train.shape[1]))
model_1.add(Activation('relu'))
model_1.add(Dropout(0.2))
model_1.add(Dense(512,kernel_regularizer=regularizers.l2(.001)))
model_1.add(Activation('relu'))
model_1.add(Dropout(0.2))
model_1.add(Dense(512,kernel_regularizer=regularizers.l2(.001)))
model_1.add(Activation('relu'))
model_1.add(Dropout(0.2))
model_1.add(Dense(128,kernel_regularizer=regularizers.l1_l2(.001)))
model_1.add(Activation('relu'))
model_1.add(Dropout(0.3))
model_1.add(Dense(32, kernel_regularizer=regularizers.l2(.001)))
model_1.add(Activation('relu'))
model_1.add(Dense(3, activation = 'softmax'))
model_1.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_47 (Dense) (None, 1024) 140288 _________________________________________________________________ activation_37 (Activation) (None, 1024) 0 _________________________________________________________________ dropout_28 (Dropout) (None, 1024) 0 _________________________________________________________________ dense_48 (Dense) (None, 512) 524800 _________________________________________________________________ activation_38 (Activation) (None, 512) 0 _________________________________________________________________ dropout_29 (Dropout) (None, 512) 0 _________________________________________________________________ dense_49 (Dense) (None, 512) 262656 _________________________________________________________________ activation_39 (Activation) (None, 512) 0 _________________________________________________________________ dropout_30 (Dropout) (None, 512) 0 _________________________________________________________________ dense_50 (Dense) (None, 128) 65664 _________________________________________________________________ activation_40 (Activation) (None, 128) 0 _________________________________________________________________ dropout_31 (Dropout) (None, 128) 0 _________________________________________________________________ dense_51 (Dense) (None, 32) 4128 _________________________________________________________________ activation_41 (Activation) (None, 32) 0 _________________________________________________________________ dense_52 (Dense) (None, 3) 99 ================================================================= Total params: 997,635 Trainable params: 997,635 Non-trainable params: 0 _________________________________________________________________
# compile the model_1
model_1.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
# checkpoint
filepath="weights-improvement-best-model_1.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks_list_1 = [checkpoint]
# training the model
start_time = time()
model_1.fit(X_train,y_train,batch_size= 1000, epochs= 250,
validation_data=(X_valid,y_valid),
#validation_split = 0.2,
verbose =2,callbacks = callbacks_list_1)
end_time = time()
Train on 40392 samples, validate on 10098 samples Epoch 1/250 - 4s - loss: 4.4220 - acc: 0.6810 - val_loss: 2.4390 - val_acc: 0.7485 Epoch 00001: val_loss improved from inf to 2.43905, saving model to weights-improvement-best-model_1.hdf5 Epoch 2/250 - 4s - loss: 1.6267 - acc: 0.7389 - val_loss: 1.0277 - val_acc: 0.7550 Epoch 00002: val_loss improved from 2.43905 to 1.02766, saving model to weights-improvement-best-model_1.hdf5 Epoch 3/250 - 4s - loss: 0.9020 - acc: 0.7473 - val_loss: 0.8926 - val_acc: 0.7160 Epoch 00003: val_loss improved from 1.02766 to 0.89259, saving model to weights-improvement-best-model_1.hdf5 Epoch 4/250 - 4s - loss: 0.8043 - acc: 0.7495 - val_loss: 0.7903 - val_acc: 0.7440 Epoch 00004: val_loss improved from 0.89259 to 0.79030, saving model to weights-improvement-best-model_1.hdf5 Epoch 5/250 - 4s - loss: 0.7647 - acc: 0.7537 - val_loss: 0.8050 - val_acc: 0.7349 Epoch 00005: val_loss did not improve from 0.79030 Epoch 6/250 - 4s - loss: 0.7497 - acc: 0.7524 - val_loss: 0.7242 - val_acc: 0.7619 Epoch 00006: val_loss improved from 0.79030 to 0.72423, saving model to weights-improvement-best-model_1.hdf5 Epoch 7/250 - 4s - loss: 0.7340 - acc: 0.7573 - val_loss: 0.7066 - val_acc: 0.7680 Epoch 00007: val_loss improved from 0.72423 to 0.70662, saving model to weights-improvement-best-model_1.hdf5 Epoch 8/250 - 4s - loss: 0.7204 - acc: 0.7596 - val_loss: 0.7035 - val_acc: 0.7660 Epoch 00008: val_loss improved from 0.70662 to 0.70354, saving model to weights-improvement-best-model_1.hdf5 Epoch 9/250 - 4s - loss: 0.7163 - acc: 0.7592 - val_loss: 0.7149 - val_acc: 0.7532 Epoch 00009: val_loss did not improve from 0.70354 Epoch 10/250 - 4s - loss: 0.7119 - acc: 0.7601 - val_loss: 0.7216 - val_acc: 0.7528 Epoch 00010: val_loss did not improve from 0.70354 Epoch 11/250 - 4s - loss: 0.7058 - acc: 0.7603 - val_loss: 0.6915 - val_acc: 0.7682 Epoch 00011: val_loss improved from 0.70354 to 0.69147, saving model to weights-improvement-best-model_1.hdf5 Epoch 12/250 - 4s - loss: 0.6997 - acc: 0.7640 - val_loss: 0.7199 - val_acc: 0.7530 Epoch 00012: val_loss did not improve from 0.69147 Epoch 13/250 - 4s - loss: 0.6944 - acc: 0.7656 - val_loss: 0.7046 - val_acc: 0.7653 Epoch 00013: val_loss did not improve from 0.69147 Epoch 14/250 - 4s - loss: 0.6904 - acc: 0.7655 - val_loss: 0.7193 - val_acc: 0.7454 Epoch 00014: val_loss did not improve from 0.69147 Epoch 15/250 - 4s - loss: 0.6861 - acc: 0.7719 - val_loss: 0.6918 - val_acc: 0.7716 Epoch 00015: val_loss did not improve from 0.69147 Epoch 16/250 - 4s - loss: 0.6835 - acc: 0.7733 - val_loss: 0.6876 - val_acc: 0.7649 Epoch 00016: val_loss improved from 0.69147 to 0.68756, saving model to weights-improvement-best-model_1.hdf5 Epoch 17/250 - 4s - loss: 0.6787 - acc: 0.7754 - val_loss: 0.6760 - val_acc: 0.7778 Epoch 00017: val_loss improved from 0.68756 to 0.67596, saving model to weights-improvement-best-model_1.hdf5 Epoch 18/250 - 4s - loss: 0.6775 - acc: 0.7763 - val_loss: 0.6823 - val_acc: 0.7703 Epoch 00018: val_loss did not improve from 0.67596 Epoch 19/250 - 4s - loss: 0.6730 - acc: 0.7779 - val_loss: 0.6894 - val_acc: 0.7732 Epoch 00019: val_loss did not improve from 0.67596 Epoch 20/250 - 4s - loss: 0.6701 - acc: 0.7798 - val_loss: 0.6727 - val_acc: 0.7801 Epoch 00020: val_loss improved from 0.67596 to 0.67267, saving model to weights-improvement-best-model_1.hdf5 Epoch 21/250 - 4s - loss: 0.6681 - acc: 0.7823 - val_loss: 0.6764 - val_acc: 0.7726 Epoch 00021: val_loss did not improve from 0.67267 Epoch 22/250 - 4s - loss: 0.6686 - acc: 0.7827 - val_loss: 0.6720 - val_acc: 0.7819 Epoch 00022: val_loss improved from 0.67267 to 0.67204, saving model to weights-improvement-best-model_1.hdf5 Epoch 23/250 - 4s - loss: 0.6644 - acc: 0.7847 - val_loss: 0.6707 - val_acc: 0.7812 Epoch 00023: val_loss improved from 0.67204 to 0.67070, saving model to weights-improvement-best-model_1.hdf5 Epoch 24/250 - 4s - loss: 0.6613 - acc: 0.7848 - val_loss: 0.6812 - val_acc: 0.7788 Epoch 00024: val_loss did not improve from 0.67070 Epoch 25/250 - 4s - loss: 0.6625 - acc: 0.7838 - val_loss: 0.6917 - val_acc: 0.7753 Epoch 00025: val_loss did not improve from 0.67070 Epoch 26/250 - 4s - loss: 0.6582 - acc: 0.7865 - val_loss: 0.6667 - val_acc: 0.7778 Epoch 00026: val_loss improved from 0.67070 to 0.66672, saving model to weights-improvement-best-model_1.hdf5 Epoch 27/250 - 4s - loss: 0.6545 - acc: 0.7874 - val_loss: 0.6755 - val_acc: 0.7765 Epoch 00027: val_loss did not improve from 0.66672 Epoch 28/250 - 4s - loss: 0.6552 - acc: 0.7889 - val_loss: 0.7000 - val_acc: 0.7702 Epoch 00028: val_loss did not improve from 0.66672 Epoch 29/250 - 4s - loss: 0.6529 - acc: 0.7875 - val_loss: 0.6706 - val_acc: 0.7801 Epoch 00029: val_loss did not improve from 0.66672 Epoch 30/250 - 4s - loss: 0.6499 - acc: 0.7905 - val_loss: 0.6612 - val_acc: 0.7839 Epoch 00030: val_loss improved from 0.66672 to 0.66121, saving model to weights-improvement-best-model_1.hdf5 Epoch 31/250 - 4s - loss: 0.6514 - acc: 0.7881 - val_loss: 0.6723 - val_acc: 0.7837 Epoch 00031: val_loss did not improve from 0.66121 Epoch 32/250 - 4s - loss: 0.6481 - acc: 0.7932 - val_loss: 0.6839 - val_acc: 0.7659 Epoch 00032: val_loss did not improve from 0.66121 Epoch 33/250 - 4s - loss: 0.6477 - acc: 0.7914 - val_loss: 0.6653 - val_acc: 0.7826 Epoch 00033: val_loss did not improve from 0.66121 Epoch 34/250 - 4s - loss: 0.6457 - acc: 0.7929 - val_loss: 0.6807 - val_acc: 0.7797 Epoch 00034: val_loss did not improve from 0.66121 Epoch 35/250 - 4s - loss: 0.6465 - acc: 0.7913 - val_loss: 0.6593 - val_acc: 0.7867 Epoch 00035: val_loss improved from 0.66121 to 0.65928, saving model to weights-improvement-best-model_1.hdf5 Epoch 36/250 - 4s - loss: 0.6422 - acc: 0.7934 - val_loss: 0.6988 - val_acc: 0.7727 Epoch 00036: val_loss did not improve from 0.65928 Epoch 37/250 - 4s - loss: 0.6448 - acc: 0.7930 - val_loss: 0.6756 - val_acc: 0.7796 Epoch 00037: val_loss did not improve from 0.65928 Epoch 38/250 - 4s - loss: 0.6415 - acc: 0.7942 - val_loss: 0.6672 - val_acc: 0.7787 Epoch 00038: val_loss did not improve from 0.65928 Epoch 39/250 - 4s - loss: 0.6387 - acc: 0.7934 - val_loss: 0.6868 - val_acc: 0.7690 Epoch 00039: val_loss did not improve from 0.65928 Epoch 40/250 - 4s - loss: 0.6400 - acc: 0.7945 - val_loss: 0.6667 - val_acc: 0.7789 Epoch 00040: val_loss did not improve from 0.65928 Epoch 41/250 - 4s - loss: 0.6366 - acc: 0.7965 - val_loss: 0.6602 - val_acc: 0.7866 Epoch 00041: val_loss did not improve from 0.65928 Epoch 42/250 - 4s - loss: 0.6354 - acc: 0.7981 - val_loss: 0.6775 - val_acc: 0.7835 Epoch 00042: val_loss did not improve from 0.65928 Epoch 43/250 - 4s - loss: 0.6345 - acc: 0.7964 - val_loss: 0.6844 - val_acc: 0.7686 Epoch 00043: val_loss did not improve from 0.65928 Epoch 44/250 - 4s - loss: 0.6355 - acc: 0.7964 - val_loss: 0.6804 - val_acc: 0.7701 Epoch 00044: val_loss did not improve from 0.65928 Epoch 45/250 - 4s - loss: 0.6312 - acc: 0.7987 - val_loss: 0.6609 - val_acc: 0.7834 Epoch 00045: val_loss did not improve from 0.65928 Epoch 46/250 - 5s - loss: 0.6325 - acc: 0.7971 - val_loss: 0.6741 - val_acc: 0.7858 Epoch 00046: val_loss did not improve from 0.65928 Epoch 47/250 - 4s - loss: 0.6331 - acc: 0.7991 - val_loss: 0.6612 - val_acc: 0.7888 Epoch 00047: val_loss did not improve from 0.65928 Epoch 48/250 - 4s - loss: 0.6331 - acc: 0.7990 - val_loss: 0.6633 - val_acc: 0.7856 Epoch 00048: val_loss did not improve from 0.65928 Epoch 49/250 - 4s - loss: 0.6287 - acc: 0.7995 - val_loss: 0.6672 - val_acc: 0.7806 Epoch 00049: val_loss did not improve from 0.65928 Epoch 50/250 - 4s - loss: 0.6242 - acc: 0.8027 - val_loss: 0.6813 - val_acc: 0.7710 Epoch 00050: val_loss did not improve from 0.65928 Epoch 51/250 - 4s - loss: 0.6269 - acc: 0.8036 - val_loss: 0.7072 - val_acc: 0.7484 Epoch 00051: val_loss did not improve from 0.65928 Epoch 52/250 - 4s - loss: 0.6251 - acc: 0.8044 - val_loss: 0.7071 - val_acc: 0.7747 Epoch 00052: val_loss did not improve from 0.65928 Epoch 53/250 - 4s - loss: 0.6260 - acc: 0.8020 - val_loss: 0.6732 - val_acc: 0.7838 Epoch 00053: val_loss did not improve from 0.65928 Epoch 54/250 - 4s - loss: 0.6245 - acc: 0.8024 - val_loss: 0.6703 - val_acc: 0.7859 Epoch 00054: val_loss did not improve from 0.65928 Epoch 55/250 - 4s - loss: 0.6253 - acc: 0.8024 - val_loss: 0.6692 - val_acc: 0.7774 Epoch 00055: val_loss did not improve from 0.65928 Epoch 56/250 - 4s - loss: 0.6245 - acc: 0.8032 - val_loss: 0.7009 - val_acc: 0.7569 Epoch 00056: val_loss did not improve from 0.65928 Epoch 57/250 - 4s - loss: 0.6235 - acc: 0.8030 - val_loss: 0.6594 - val_acc: 0.7863 Epoch 00057: val_loss did not improve from 0.65928 Epoch 58/250 - 4s - loss: 0.6213 - acc: 0.8037 - val_loss: 0.6613 - val_acc: 0.7837 Epoch 00058: val_loss did not improve from 0.65928 Epoch 59/250 - 4s - loss: 0.6247 - acc: 0.8025 - val_loss: 0.6723 - val_acc: 0.7878 Epoch 00059: val_loss did not improve from 0.65928 Epoch 60/250 - 4s - loss: 0.6228 - acc: 0.8022 - val_loss: 0.6675 - val_acc: 0.7903 Epoch 00060: val_loss did not improve from 0.65928 Epoch 61/250 - 4s - loss: 0.6205 - acc: 0.8036 - val_loss: 0.6756 - val_acc: 0.7701 Epoch 00061: val_loss did not improve from 0.65928 Epoch 62/250 - 4s - loss: 0.6152 - acc: 0.8075 - val_loss: 0.6689 - val_acc: 0.7768 Epoch 00062: val_loss did not improve from 0.65928 Epoch 63/250 - 4s - loss: 0.6193 - acc: 0.8055 - val_loss: 0.6674 - val_acc: 0.7760 Epoch 00063: val_loss did not improve from 0.65928 Epoch 64/250 - 4s - loss: 0.6187 - acc: 0.8050 - val_loss: 0.6621 - val_acc: 0.7806 Epoch 00064: val_loss did not improve from 0.65928 Epoch 65/250 - 4s - loss: 0.6184 - acc: 0.8043 - val_loss: 0.6570 - val_acc: 0.7879 Epoch 00065: val_loss improved from 0.65928 to 0.65702, saving model to weights-improvement-best-model_1.hdf5 Epoch 66/250 - 4s - loss: 0.6150 - acc: 0.8072 - val_loss: 0.6606 - val_acc: 0.7867 Epoch 00066: val_loss did not improve from 0.65702 Epoch 67/250 - 4s - loss: 0.6117 - acc: 0.8092 - val_loss: 0.6728 - val_acc: 0.7798 Epoch 00067: val_loss did not improve from 0.65702 Epoch 68/250 - 4s - loss: 0.6134 - acc: 0.8088 - val_loss: 0.7106 - val_acc: 0.7780 Epoch 00068: val_loss did not improve from 0.65702 Epoch 69/250 - 4s - loss: 0.6176 - acc: 0.8064 - val_loss: 0.6668 - val_acc: 0.7881 Epoch 00069: val_loss did not improve from 0.65702 Epoch 70/250 - 4s - loss: 0.6127 - acc: 0.8102 - val_loss: 0.6873 - val_acc: 0.7808 Epoch 00070: val_loss did not improve from 0.65702 Epoch 71/250 - 4s - loss: 0.6143 - acc: 0.8091 - val_loss: 0.6600 - val_acc: 0.7868 Epoch 00071: val_loss did not improve from 0.65702 Epoch 72/250 - 4s - loss: 0.6143 - acc: 0.8069 - val_loss: 0.6848 - val_acc: 0.7644 Epoch 00072: val_loss did not improve from 0.65702 Epoch 73/250 - 4s - loss: 0.6069 - acc: 0.8100 - val_loss: 0.6659 - val_acc: 0.7788 Epoch 00073: val_loss did not improve from 0.65702 Epoch 74/250 - 4s - loss: 0.6123 - acc: 0.8080 - val_loss: 0.6870 - val_acc: 0.7631 Epoch 00074: val_loss did not improve from 0.65702 Epoch 75/250 - 4s - loss: 0.6146 - acc: 0.8067 - val_loss: 0.6937 - val_acc: 0.7606 Epoch 00075: val_loss did not improve from 0.65702 Epoch 76/250 - 4s - loss: 0.6096 - acc: 0.8085 - val_loss: 0.6685 - val_acc: 0.7807 Epoch 00076: val_loss did not improve from 0.65702 Epoch 77/250 - 4s - loss: 0.6134 - acc: 0.8092 - val_loss: 0.6628 - val_acc: 0.7859 Epoch 00077: val_loss did not improve from 0.65702 Epoch 78/250 - 4s - loss: 0.6116 - acc: 0.8088 - val_loss: 0.6612 - val_acc: 0.7887 Epoch 00078: val_loss did not improve from 0.65702 Epoch 79/250 - 4s - loss: 0.6083 - acc: 0.8108 - val_loss: 0.6785 - val_acc: 0.7890 Epoch 00079: val_loss did not improve from 0.65702 Epoch 80/250 - 4s - loss: 0.6066 - acc: 0.8115 - val_loss: 0.6801 - val_acc: 0.7869 Epoch 00080: val_loss did not improve from 0.65702 Epoch 81/250 - 4s - loss: 0.6082 - acc: 0.8118 - val_loss: 0.6742 - val_acc: 0.7871 Epoch 00081: val_loss did not improve from 0.65702 Epoch 82/250 - 4s - loss: 0.6077 - acc: 0.8113 - val_loss: 0.7121 - val_acc: 0.7755 Epoch 00082: val_loss did not improve from 0.65702 Epoch 83/250 - 4s - loss: 0.6078 - acc: 0.8098 - val_loss: 0.6638 - val_acc: 0.7824 Epoch 00083: val_loss did not improve from 0.65702 Epoch 84/250 - 4s - loss: 0.6077 - acc: 0.8114 - val_loss: 0.6678 - val_acc: 0.7913 Epoch 00084: val_loss did not improve from 0.65702 Epoch 85/250 - 4s - loss: 0.6039 - acc: 0.8128 - val_loss: 0.6741 - val_acc: 0.7757 Epoch 00085: val_loss did not improve from 0.65702 Epoch 86/250 - 4s - loss: 0.6043 - acc: 0.8125 - val_loss: 0.6885 - val_acc: 0.7834 Epoch 00086: val_loss did not improve from 0.65702 Epoch 87/250 - 4s - loss: 0.6029 - acc: 0.8137 - val_loss: 0.6719 - val_acc: 0.7888 Epoch 00087: val_loss did not improve from 0.65702 Epoch 88/250 - 4s - loss: 0.6054 - acc: 0.8124 - val_loss: 0.6961 - val_acc: 0.7595 Epoch 00088: val_loss did not improve from 0.65702 Epoch 89/250 - 4s - loss: 0.6026 - acc: 0.8126 - val_loss: 0.6799 - val_acc: 0.7734 Epoch 00089: val_loss did not improve from 0.65702 Epoch 90/250 - 4s - loss: 0.5995 - acc: 0.8153 - val_loss: 0.7145 - val_acc: 0.7506 Epoch 00090: val_loss did not improve from 0.65702 Epoch 91/250 - 4s - loss: 0.6036 - acc: 0.8140 - val_loss: 0.6644 - val_acc: 0.7840 Epoch 00091: val_loss did not improve from 0.65702 Epoch 92/250 - 4s - loss: 0.6061 - acc: 0.8128 - val_loss: 0.6613 - val_acc: 0.7873 Epoch 00092: val_loss did not improve from 0.65702 Epoch 93/250 - 4s - loss: 0.6035 - acc: 0.8128 - val_loss: 0.6805 - val_acc: 0.7735 Epoch 00093: val_loss did not improve from 0.65702 Epoch 94/250 - 4s - loss: 0.6033 - acc: 0.8145 - val_loss: 0.6629 - val_acc: 0.7858 Epoch 00094: val_loss did not improve from 0.65702 Epoch 95/250 - 4s - loss: 0.6045 - acc: 0.8135 - val_loss: 0.6646 - val_acc: 0.7889 Epoch 00095: val_loss did not improve from 0.65702 Epoch 96/250 - 4s - loss: 0.6027 - acc: 0.8146 - val_loss: 0.6793 - val_acc: 0.7749 Epoch 00096: val_loss did not improve from 0.65702 Epoch 97/250 - 4s - loss: 0.6016 - acc: 0.8135 - val_loss: 0.6845 - val_acc: 0.7748 Epoch 00097: val_loss did not improve from 0.65702 Epoch 98/250 - 4s - loss: 0.6025 - acc: 0.8139 - val_loss: 0.6607 - val_acc: 0.7898 Epoch 00098: val_loss did not improve from 0.65702 Epoch 99/250 - 4s - loss: 0.5978 - acc: 0.8139 - val_loss: 0.6992 - val_acc: 0.7609 Epoch 00099: val_loss did not improve from 0.65702 Epoch 100/250 - 4s - loss: 0.6027 - acc: 0.8140 - val_loss: 0.6645 - val_acc: 0.7820 Epoch 00100: val_loss did not improve from 0.65702 Epoch 101/250 - 4s - loss: 0.6003 - acc: 0.8146 - val_loss: 0.6751 - val_acc: 0.7882 Epoch 00101: val_loss did not improve from 0.65702 Epoch 102/250 - 4s - loss: 0.5975 - acc: 0.8172 - val_loss: 0.6691 - val_acc: 0.7795 Epoch 00102: val_loss did not improve from 0.65702 Epoch 103/250 - 4s - loss: 0.5995 - acc: 0.8151 - val_loss: 0.6974 - val_acc: 0.7836 Epoch 00103: val_loss did not improve from 0.65702 Epoch 104/250 - 4s - loss: 0.5969 - acc: 0.8157 - val_loss: 0.6761 - val_acc: 0.7772 Epoch 00104: val_loss did not improve from 0.65702 Epoch 105/250 - 4s - loss: 0.5993 - acc: 0.8155 - val_loss: 0.6748 - val_acc: 0.7788 Epoch 00105: val_loss did not improve from 0.65702 Epoch 106/250 - 4s - loss: 0.5998 - acc: 0.8144 - val_loss: 0.6665 - val_acc: 0.7843 Epoch 00106: val_loss did not improve from 0.65702 Epoch 107/250 - 4s - loss: 0.5960 - acc: 0.8163 - val_loss: 0.6602 - val_acc: 0.7848 Epoch 00107: val_loss did not improve from 0.65702 Epoch 108/250 - 4s - loss: 0.5958 - acc: 0.8169 - val_loss: 0.6684 - val_acc: 0.7836 Epoch 00108: val_loss did not improve from 0.65702 Epoch 109/250 - 4s - loss: 0.5945 - acc: 0.8181 - val_loss: 0.6733 - val_acc: 0.7776 Epoch 00109: val_loss did not improve from 0.65702 Epoch 110/250 - 4s - loss: 0.5914 - acc: 0.8202 - val_loss: 0.6917 - val_acc: 0.7670 Epoch 00110: val_loss did not improve from 0.65702 Epoch 111/250 - 4s - loss: 0.5980 - acc: 0.8162 - val_loss: 0.7066 - val_acc: 0.7833 Epoch 00111: val_loss did not improve from 0.65702 Epoch 112/250 - 4s - loss: 0.5941 - acc: 0.8177 - val_loss: 0.6873 - val_acc: 0.7703 Epoch 00112: val_loss did not improve from 0.65702 Epoch 113/250 - 4s - loss: 0.5965 - acc: 0.8173 - val_loss: 0.6670 - val_acc: 0.7821 Epoch 00113: val_loss did not improve from 0.65702 Epoch 114/250 - 4s - loss: 0.5950 - acc: 0.8195 - val_loss: 0.6765 - val_acc: 0.7745 Epoch 00114: val_loss did not improve from 0.65702 Epoch 115/250 - 4s - loss: 0.5932 - acc: 0.8196 - val_loss: 0.6653 - val_acc: 0.7895 Epoch 00115: val_loss did not improve from 0.65702 Epoch 116/250 - 4s - loss: 0.5900 - acc: 0.8203 - val_loss: 0.6707 - val_acc: 0.7858 Epoch 00116: val_loss did not improve from 0.65702 Epoch 117/250 - 4s - loss: 0.5950 - acc: 0.8187 - val_loss: 0.6738 - val_acc: 0.7860 Epoch 00117: val_loss did not improve from 0.65702 Epoch 118/250 - 4s - loss: 0.5953 - acc: 0.8177 - val_loss: 0.6944 - val_acc: 0.7890 Epoch 00118: val_loss did not improve from 0.65702 Epoch 119/250 - 4s - loss: 0.5938 - acc: 0.8175 - val_loss: 0.6872 - val_acc: 0.7896 Epoch 00119: val_loss did not improve from 0.65702 Epoch 120/250 - 4s - loss: 0.5913 - acc: 0.8189 - val_loss: 0.7274 - val_acc: 0.7811 Epoch 00120: val_loss did not improve from 0.65702 Epoch 121/250 - 4s - loss: 0.5927 - acc: 0.8188 - val_loss: 0.6707 - val_acc: 0.7810 Epoch 00121: val_loss did not improve from 0.65702 Epoch 122/250 - 4s - loss: 0.5903 - acc: 0.8202 - val_loss: 0.6749 - val_acc: 0.7812 Epoch 00122: val_loss did not improve from 0.65702 Epoch 123/250 - 4s - loss: 0.5950 - acc: 0.8214 - val_loss: 0.6780 - val_acc: 0.7703 Epoch 00123: val_loss did not improve from 0.65702 Epoch 124/250 - 4s - loss: 0.5901 - acc: 0.8201 - val_loss: 0.6827 - val_acc: 0.7868 Epoch 00124: val_loss did not improve from 0.65702 Epoch 125/250 - 4s - loss: 0.5896 - acc: 0.8211 - val_loss: 0.6751 - val_acc: 0.7869 Epoch 00125: val_loss did not improve from 0.65702 Epoch 126/250 - 4s - loss: 0.5873 - acc: 0.8220 - val_loss: 0.7397 - val_acc: 0.7753 Epoch 00126: val_loss did not improve from 0.65702 Epoch 127/250 - 4s - loss: 0.5906 - acc: 0.8211 - val_loss: 0.6698 - val_acc: 0.7855 Epoch 00127: val_loss did not improve from 0.65702 Epoch 128/250 - 4s - loss: 0.5887 - acc: 0.8224 - val_loss: 0.6822 - val_acc: 0.7751 Epoch 00128: val_loss did not improve from 0.65702 Epoch 129/250 - 4s - loss: 0.5874 - acc: 0.8213 - val_loss: 0.6745 - val_acc: 0.7893 Epoch 00129: val_loss did not improve from 0.65702 Epoch 130/250 - 4s - loss: 0.5897 - acc: 0.8191 - val_loss: 0.6770 - val_acc: 0.7885 Epoch 00130: val_loss did not improve from 0.65702 Epoch 131/250 - 4s - loss: 0.5881 - acc: 0.8209 - val_loss: 0.6698 - val_acc: 0.7802 Epoch 00131: val_loss did not improve from 0.65702 Epoch 132/250 - 4s - loss: 0.5861 - acc: 0.8220 - val_loss: 0.6927 - val_acc: 0.7942 Epoch 00132: val_loss did not improve from 0.65702 Epoch 133/250 - 4s - loss: 0.5914 - acc: 0.8198 - val_loss: 0.6803 - val_acc: 0.7822 Epoch 00133: val_loss did not improve from 0.65702 Epoch 134/250 - 4s - loss: 0.5888 - acc: 0.8219 - val_loss: 0.6908 - val_acc: 0.7915 Epoch 00134: val_loss did not improve from 0.65702 Epoch 135/250 - 4s - loss: 0.5850 - acc: 0.8227 - val_loss: 0.6747 - val_acc: 0.7838 Epoch 00135: val_loss did not improve from 0.65702 Epoch 136/250 - 4s - loss: 0.5888 - acc: 0.8214 - val_loss: 0.7014 - val_acc: 0.7627 Epoch 00136: val_loss did not improve from 0.65702 Epoch 137/250 - 5s - loss: 0.5872 - acc: 0.8221 - val_loss: 0.7058 - val_acc: 0.7602 Epoch 00137: val_loss did not improve from 0.65702 Epoch 138/250 - 4s - loss: 0.5877 - acc: 0.8216 - val_loss: 0.6745 - val_acc: 0.7822 Epoch 00138: val_loss did not improve from 0.65702 Epoch 139/250 - 4s - loss: 0.5889 - acc: 0.8218 - val_loss: 0.6836 - val_acc: 0.7788 Epoch 00139: val_loss did not improve from 0.65702 Epoch 140/250 - 4s - loss: 0.5845 - acc: 0.8221 - val_loss: 0.6753 - val_acc: 0.7849 Epoch 00140: val_loss did not improve from 0.65702 Epoch 141/250 - 4s - loss: 0.5908 - acc: 0.8200 - val_loss: 0.6945 - val_acc: 0.7745 Epoch 00141: val_loss did not improve from 0.65702 Epoch 142/250 - 4s - loss: 0.5875 - acc: 0.8191 - val_loss: 0.6832 - val_acc: 0.7916 Epoch 00142: val_loss did not improve from 0.65702 Epoch 143/250 - 4s - loss: 0.5873 - acc: 0.8225 - val_loss: 0.6813 - val_acc: 0.7774 Epoch 00143: val_loss did not improve from 0.65702 Epoch 144/250 - 4s - loss: 0.5873 - acc: 0.8226 - val_loss: 0.7055 - val_acc: 0.7920 Epoch 00144: val_loss did not improve from 0.65702 Epoch 145/250 - 4s - loss: 0.5838 - acc: 0.8237 - val_loss: 0.6789 - val_acc: 0.7844 Epoch 00145: val_loss did not improve from 0.65702 Epoch 146/250 - 4s - loss: 0.5863 - acc: 0.8221 - val_loss: 0.6801 - val_acc: 0.7870 Epoch 00146: val_loss did not improve from 0.65702 Epoch 147/250 - 4s - loss: 0.5825 - acc: 0.8233 - val_loss: 0.6975 - val_acc: 0.7888 Epoch 00147: val_loss did not improve from 0.65702 Epoch 148/250 - 4s - loss: 0.5887 - acc: 0.8205 - val_loss: 0.6883 - val_acc: 0.7870 Epoch 00148: val_loss did not improve from 0.65702 Epoch 149/250 - 4s - loss: 0.5863 - acc: 0.8195 - val_loss: 0.6727 - val_acc: 0.7911 Epoch 00149: val_loss did not improve from 0.65702 Epoch 150/250 - 4s - loss: 0.5826 - acc: 0.8245 - val_loss: 0.6696 - val_acc: 0.7891 Epoch 00150: val_loss did not improve from 0.65702 Epoch 151/250 - 4s - loss: 0.5873 - acc: 0.8208 - val_loss: 0.6960 - val_acc: 0.7914 Epoch 00151: val_loss did not improve from 0.65702 Epoch 152/250 - 4s - loss: 0.5838 - acc: 0.8234 - val_loss: 0.6848 - val_acc: 0.7908 Epoch 00152: val_loss did not improve from 0.65702 Epoch 153/250 - 4s - loss: 0.5850 - acc: 0.8233 - val_loss: 0.6950 - val_acc: 0.7708 Epoch 00153: val_loss did not improve from 0.65702 Epoch 154/250 - 4s - loss: 0.5834 - acc: 0.8237 - val_loss: 0.6739 - val_acc: 0.7842 Epoch 00154: val_loss did not improve from 0.65702 Epoch 155/250 - 4s - loss: 0.5803 - acc: 0.8250 - val_loss: 0.6959 - val_acc: 0.7923 Epoch 00155: val_loss did not improve from 0.65702 Epoch 156/250 - 4s - loss: 0.5865 - acc: 0.8222 - val_loss: 0.6752 - val_acc: 0.7879 Epoch 00156: val_loss did not improve from 0.65702 Epoch 157/250 - 4s - loss: 0.5781 - acc: 0.8257 - val_loss: 0.6930 - val_acc: 0.7645 Epoch 00157: val_loss did not improve from 0.65702 Epoch 158/250 - 4s - loss: 0.5861 - acc: 0.8236 - val_loss: 0.6750 - val_acc: 0.7877 Epoch 00158: val_loss did not improve from 0.65702 Epoch 159/250 - 4s - loss: 0.5836 - acc: 0.8253 - val_loss: 0.6828 - val_acc: 0.7782 Epoch 00159: val_loss did not improve from 0.65702 Epoch 160/250 - 4s - loss: 0.5810 - acc: 0.8240 - val_loss: 0.6879 - val_acc: 0.7775 Epoch 00160: val_loss did not improve from 0.65702 Epoch 161/250 - 4s - loss: 0.5819 - acc: 0.8233 - val_loss: 0.6908 - val_acc: 0.7671 Epoch 00161: val_loss did not improve from 0.65702 Epoch 162/250 - 4s - loss: 0.5824 - acc: 0.8246 - val_loss: 0.6802 - val_acc: 0.7819 Epoch 00162: val_loss did not improve from 0.65702 Epoch 163/250 - 5s - loss: 0.5823 - acc: 0.8238 - val_loss: 0.6772 - val_acc: 0.7833 Epoch 00163: val_loss did not improve from 0.65702 Epoch 164/250 - 4s - loss: 0.5823 - acc: 0.8250 - val_loss: 0.6810 - val_acc: 0.7725 Epoch 00164: val_loss did not improve from 0.65702 Epoch 165/250 - 5s - loss: 0.5820 - acc: 0.8240 - val_loss: 0.6692 - val_acc: 0.7858 Epoch 00165: val_loss did not improve from 0.65702 Epoch 166/250 - 5s - loss: 0.5812 - acc: 0.8266 - val_loss: 0.6857 - val_acc: 0.7929 Epoch 00166: val_loss did not improve from 0.65702 Epoch 167/250 - 4s - loss: 0.5819 - acc: 0.8240 - val_loss: 0.6911 - val_acc: 0.7915 Epoch 00167: val_loss did not improve from 0.65702 Epoch 168/250 - 4s - loss: 0.5828 - acc: 0.8246 - val_loss: 0.6831 - val_acc: 0.7765 Epoch 00168: val_loss did not improve from 0.65702 Epoch 169/250 - 5s - loss: 0.5812 - acc: 0.8243 - val_loss: 0.7069 - val_acc: 0.7653 Epoch 00169: val_loss did not improve from 0.65702 Epoch 170/250 - 4s - loss: 0.5794 - acc: 0.8252 - val_loss: 0.6993 - val_acc: 0.7879 Epoch 00170: val_loss did not improve from 0.65702 Epoch 171/250 - 4s - loss: 0.5835 - acc: 0.8246 - val_loss: 0.6932 - val_acc: 0.7883 Epoch 00171: val_loss did not improve from 0.65702 Epoch 172/250 - 5s - loss: 0.5771 - acc: 0.8275 - val_loss: 0.7608 - val_acc: 0.7737 Epoch 00172: val_loss did not improve from 0.65702 Epoch 173/250 - 4s - loss: 0.5787 - acc: 0.8258 - val_loss: 0.6883 - val_acc: 0.7930 Epoch 00173: val_loss did not improve from 0.65702 Epoch 174/250 - 4s - loss: 0.5828 - acc: 0.8253 - val_loss: 0.6965 - val_acc: 0.7788 Epoch 00174: val_loss did not improve from 0.65702 Epoch 175/250 - 4s - loss: 0.5814 - acc: 0.8258 - val_loss: 0.6972 - val_acc: 0.7892 Epoch 00175: val_loss did not improve from 0.65702 Epoch 176/250 - 4s - loss: 0.5748 - acc: 0.8301 - val_loss: 0.6975 - val_acc: 0.7902 Epoch 00176: val_loss did not improve from 0.65702 Epoch 177/250 - 4s - loss: 0.5802 - acc: 0.8256 - val_loss: 0.6756 - val_acc: 0.7825 Epoch 00177: val_loss did not improve from 0.65702 Epoch 178/250 - 5s - loss: 0.5830 - acc: 0.8227 - val_loss: 0.6858 - val_acc: 0.7859 Epoch 00178: val_loss did not improve from 0.65702 Epoch 179/250 - 6s - loss: 0.5803 - acc: 0.8250 - val_loss: 0.6781 - val_acc: 0.7780 Epoch 00179: val_loss did not improve from 0.65702 Epoch 180/250 - 5s - loss: 0.5784 - acc: 0.8254 - val_loss: 0.7013 - val_acc: 0.7902 Epoch 00180: val_loss did not improve from 0.65702 Epoch 181/250 - 4s - loss: 0.5793 - acc: 0.8249 - val_loss: 0.6755 - val_acc: 0.7853 Epoch 00181: val_loss did not improve from 0.65702 Epoch 182/250 - 4s - loss: 0.5769 - acc: 0.8275 - val_loss: 0.7053 - val_acc: 0.7708 Epoch 00182: val_loss did not improve from 0.65702 Epoch 183/250 - 4s - loss: 0.5798 - acc: 0.8249 - val_loss: 0.6885 - val_acc: 0.7744 Epoch 00183: val_loss did not improve from 0.65702 Epoch 184/250 - 4s - loss: 0.5760 - acc: 0.8276 - val_loss: 0.6985 - val_acc: 0.7902 Epoch 00184: val_loss did not improve from 0.65702 Epoch 185/250 - 4s - loss: 0.5770 - acc: 0.8280 - val_loss: 0.7102 - val_acc: 0.7883 Epoch 00185: val_loss did not improve from 0.65702 Epoch 186/250 - 4s - loss: 0.5793 - acc: 0.8264 - val_loss: 0.6798 - val_acc: 0.7851 Epoch 00186: val_loss did not improve from 0.65702 Epoch 187/250 - 4s - loss: 0.5753 - acc: 0.8277 - val_loss: 0.6803 - val_acc: 0.7893 Epoch 00187: val_loss did not improve from 0.65702 Epoch 188/250 - 4s - loss: 0.5785 - acc: 0.8280 - val_loss: 0.7331 - val_acc: 0.7506 Epoch 00188: val_loss did not improve from 0.65702 Epoch 189/250 - 4s - loss: 0.5778 - acc: 0.8258 - val_loss: 0.6888 - val_acc: 0.7878 Epoch 00189: val_loss did not improve from 0.65702 Epoch 190/250 - 4s - loss: 0.5794 - acc: 0.8262 - val_loss: 0.7142 - val_acc: 0.7850 Epoch 00190: val_loss did not improve from 0.65702 Epoch 191/250 - 4s - loss: 0.5735 - acc: 0.8288 - val_loss: 0.6927 - val_acc: 0.7895 Epoch 00191: val_loss did not improve from 0.65702 Epoch 192/250 - 5s - loss: 0.5801 - acc: 0.8260 - val_loss: 0.6875 - val_acc: 0.7734 Epoch 00192: val_loss did not improve from 0.65702 Epoch 193/250 - 4s - loss: 0.5736 - acc: 0.8285 - val_loss: 0.7012 - val_acc: 0.7653 Epoch 00193: val_loss did not improve from 0.65702 Epoch 194/250 - 4s - loss: 0.5755 - acc: 0.8269 - val_loss: 0.6909 - val_acc: 0.7910 Epoch 00194: val_loss did not improve from 0.65702 Epoch 195/250 - 5s - loss: 0.5786 - acc: 0.8258 - val_loss: 0.6909 - val_acc: 0.7893 Epoch 00195: val_loss did not improve from 0.65702 Epoch 196/250 - 4s - loss: 0.5768 - acc: 0.8263 - val_loss: 0.7005 - val_acc: 0.7649 Epoch 00196: val_loss did not improve from 0.65702 Epoch 197/250 - 4s - loss: 0.5737 - acc: 0.8293 - val_loss: 0.6828 - val_acc: 0.7759 Epoch 00197: val_loss did not improve from 0.65702 Epoch 198/250 - 4s - loss: 0.5780 - acc: 0.8297 - val_loss: 0.6853 - val_acc: 0.7788 Epoch 00198: val_loss did not improve from 0.65702 Epoch 199/250 - 4s - loss: 0.5778 - acc: 0.8261 - val_loss: 0.6752 - val_acc: 0.7900 Epoch 00199: val_loss did not improve from 0.65702 Epoch 200/250 - 4s - loss: 0.5739 - acc: 0.8291 - val_loss: 0.7097 - val_acc: 0.7599 Epoch 00200: val_loss did not improve from 0.65702 Epoch 201/250 - 4s - loss: 0.5777 - acc: 0.8275 - val_loss: 0.6968 - val_acc: 0.7854 Epoch 00201: val_loss did not improve from 0.65702 Epoch 202/250 - 4s - loss: 0.5759 - acc: 0.8292 - val_loss: 0.6883 - val_acc: 0.7874 Epoch 00202: val_loss did not improve from 0.65702 Epoch 203/250 - 4s - loss: 0.5748 - acc: 0.8285 - val_loss: 0.6921 - val_acc: 0.7873 Epoch 00203: val_loss did not improve from 0.65702 Epoch 204/250 - 4s - loss: 0.5786 - acc: 0.8255 - val_loss: 0.6754 - val_acc: 0.7851 Epoch 00204: val_loss did not improve from 0.65702 Epoch 205/250 - 4s - loss: 0.5681 - acc: 0.8309 - val_loss: 0.7676 - val_acc: 0.7805 Epoch 00205: val_loss did not improve from 0.65702 Epoch 206/250 - 4s - loss: 0.5778 - acc: 0.8280 - val_loss: 0.6804 - val_acc: 0.7891 Epoch 00206: val_loss did not improve from 0.65702 Epoch 207/250 - 4s - loss: 0.5744 - acc: 0.8278 - val_loss: 0.7137 - val_acc: 0.7888 Epoch 00207: val_loss did not improve from 0.65702 Epoch 208/250 - 4s - loss: 0.5760 - acc: 0.8275 - val_loss: 0.7175 - val_acc: 0.7557 Epoch 00208: val_loss did not improve from 0.65702 Epoch 209/250 - 4s - loss: 0.5718 - acc: 0.8305 - val_loss: 0.6826 - val_acc: 0.7873 Epoch 00209: val_loss did not improve from 0.65702 Epoch 210/250 - 4s - loss: 0.5721 - acc: 0.8290 - val_loss: 0.6976 - val_acc: 0.7804 Epoch 00210: val_loss did not improve from 0.65702 Epoch 211/250 - 4s - loss: 0.5722 - acc: 0.8311 - val_loss: 0.6872 - val_acc: 0.7893 Epoch 00211: val_loss did not improve from 0.65702 Epoch 212/250 - 4s - loss: 0.5748 - acc: 0.8274 - val_loss: 0.6891 - val_acc: 0.7828 Epoch 00212: val_loss did not improve from 0.65702 Epoch 213/250 - 4s - loss: 0.5730 - acc: 0.8300 - val_loss: 0.6824 - val_acc: 0.7875 Epoch 00213: val_loss did not improve from 0.65702 Epoch 214/250 - 5s - loss: 0.5741 - acc: 0.8289 - val_loss: 0.6836 - val_acc: 0.7787 Epoch 00214: val_loss did not improve from 0.65702 Epoch 215/250 - 4s - loss: 0.5741 - acc: 0.8309 - val_loss: 0.6972 - val_acc: 0.7898 Epoch 00215: val_loss did not improve from 0.65702 Epoch 216/250 - 5s - loss: 0.5768 - acc: 0.8292 - val_loss: 0.6932 - val_acc: 0.7811 Epoch 00216: val_loss did not improve from 0.65702 Epoch 217/250 - 5s - loss: 0.5718 - acc: 0.8300 - val_loss: 0.6903 - val_acc: 0.7752 Epoch 00217: val_loss did not improve from 0.65702 Epoch 218/250 - 4s - loss: 0.5729 - acc: 0.8283 - val_loss: 0.7024 - val_acc: 0.7897 Epoch 00218: val_loss did not improve from 0.65702 Epoch 219/250 - 4s - loss: 0.5697 - acc: 0.8300 - val_loss: 0.6943 - val_acc: 0.7801 Epoch 00219: val_loss did not improve from 0.65702 Epoch 220/250 - 5s - loss: 0.5723 - acc: 0.8306 - val_loss: 0.6905 - val_acc: 0.7730 Epoch 00220: val_loss did not improve from 0.65702 Epoch 221/250 - 5s - loss: 0.5732 - acc: 0.8292 - val_loss: 0.6935 - val_acc: 0.7797 Epoch 00221: val_loss did not improve from 0.65702 Epoch 222/250 - 5s - loss: 0.5721 - acc: 0.8297 - val_loss: 0.7087 - val_acc: 0.7895 Epoch 00222: val_loss did not improve from 0.65702 Epoch 223/250 - 4s - loss: 0.5701 - acc: 0.8315 - val_loss: 0.7228 - val_acc: 0.7876 Epoch 00223: val_loss did not improve from 0.65702 Epoch 224/250 - 4s - loss: 0.5766 - acc: 0.8271 - val_loss: 0.7164 - val_acc: 0.7896 Epoch 00224: val_loss did not improve from 0.65702 Epoch 225/250 - 4s - loss: 0.5740 - acc: 0.8291 - val_loss: 0.6929 - val_acc: 0.7878 Epoch 00225: val_loss did not improve from 0.65702 Epoch 226/250 - 5s - loss: 0.5734 - acc: 0.8284 - val_loss: 0.6830 - val_acc: 0.7833 Epoch 00226: val_loss did not improve from 0.65702 Epoch 227/250 - 4s - loss: 0.5727 - acc: 0.8289 - val_loss: 0.6832 - val_acc: 0.7852 Epoch 00227: val_loss did not improve from 0.65702 Epoch 228/250 - 4s - loss: 0.5740 - acc: 0.8297 - val_loss: 0.6780 - val_acc: 0.7896 Epoch 00228: val_loss did not improve from 0.65702 Epoch 229/250 - 4s - loss: 0.5696 - acc: 0.8318 - val_loss: 0.7061 - val_acc: 0.7809 Epoch 00229: val_loss did not improve from 0.65702 Epoch 230/250 - 4s - loss: 0.5740 - acc: 0.8315 - val_loss: 0.6906 - val_acc: 0.7934 Epoch 00230: val_loss did not improve from 0.65702 Epoch 231/250 - 5s - loss: 0.5714 - acc: 0.8308 - val_loss: 0.6869 - val_acc: 0.7806 Epoch 00231: val_loss did not improve from 0.65702 Epoch 232/250 - 4s - loss: 0.5681 - acc: 0.8331 - val_loss: 0.6855 - val_acc: 0.7892 Epoch 00232: val_loss did not improve from 0.65702 Epoch 233/250 - 4s - loss: 0.5686 - acc: 0.8318 - val_loss: 0.6915 - val_acc: 0.7920 Epoch 00233: val_loss did not improve from 0.65702 Epoch 234/250 - 4s - loss: 0.5719 - acc: 0.8319 - val_loss: 0.7078 - val_acc: 0.7708 Epoch 00234: val_loss did not improve from 0.65702 Epoch 235/250 - 4s - loss: 0.5718 - acc: 0.8309 - val_loss: 0.7037 - val_acc: 0.7921 Epoch 00235: val_loss did not improve from 0.65702 Epoch 236/250 - 5s - loss: 0.5698 - acc: 0.8312 - val_loss: 0.7048 - val_acc: 0.7644 Epoch 00236: val_loss did not improve from 0.65702 Epoch 237/250 - 5s - loss: 0.5734 - acc: 0.8303 - val_loss: 0.6826 - val_acc: 0.7813 Epoch 00237: val_loss did not improve from 0.65702 Epoch 238/250 - 5s - loss: 0.5682 - acc: 0.8320 - val_loss: 0.7631 - val_acc: 0.7467 Epoch 00238: val_loss did not improve from 0.65702 Epoch 239/250 - 5s - loss: 0.5752 - acc: 0.8287 - val_loss: 0.6881 - val_acc: 0.7903 Epoch 00239: val_loss did not improve from 0.65702 Epoch 240/250 - 5s - loss: 0.5690 - acc: 0.8314 - val_loss: 0.7031 - val_acc: 0.7908 Epoch 00240: val_loss did not improve from 0.65702 Epoch 241/250 - 4s - loss: 0.5728 - acc: 0.8286 - val_loss: 0.6775 - val_acc: 0.7859 Epoch 00241: val_loss did not improve from 0.65702 Epoch 242/250 - 5s - loss: 0.5687 - acc: 0.8318 - val_loss: 0.6821 - val_acc: 0.7900 Epoch 00242: val_loss did not improve from 0.65702 Epoch 243/250 - 5s - loss: 0.5702 - acc: 0.8296 - val_loss: 0.6919 - val_acc: 0.7932 Epoch 00243: val_loss did not improve from 0.65702 Epoch 244/250 - 4s - loss: 0.5710 - acc: 0.8305 - val_loss: 0.6828 - val_acc: 0.7817 Epoch 00244: val_loss did not improve from 0.65702 Epoch 245/250 - 5s - loss: 0.5696 - acc: 0.8310 - val_loss: 0.6866 - val_acc: 0.7808 Epoch 00245: val_loss did not improve from 0.65702 Epoch 246/250 - 4s - loss: 0.5707 - acc: 0.8297 - val_loss: 0.6880 - val_acc: 0.7865 Epoch 00246: val_loss did not improve from 0.65702 Epoch 247/250 - 5s - loss: 0.5717 - acc: 0.8296 - val_loss: 0.7251 - val_acc: 0.7577 Epoch 00247: val_loss did not improve from 0.65702 Epoch 248/250 - 5s - loss: 0.5715 - acc: 0.8299 - val_loss: 0.6897 - val_acc: 0.7847 Epoch 00248: val_loss did not improve from 0.65702 Epoch 249/250 - 5s - loss: 0.5684 - acc: 0.8316 - val_loss: 0.7144 - val_acc: 0.7870 Epoch 00249: val_loss did not improve from 0.65702 Epoch 250/250 - 4s - loss: 0.5693 - acc: 0.8295 - val_loss: 0.6912 - val_acc: 0.7876 Epoch 00250: val_loss did not improve from 0.65702
# trying another architechture : model_2 with different optimizer
model_2 = Sequential()
model_2.add(Dense(1024, kernel_regularizer=regularizers.l2(.001),input_dim=X_train.shape[1]))
model_2.add(Activation('relu'))
model_2.add(Dropout(0.2))
model_2.add(Dense(512,kernel_regularizer=regularizers.l2(.001)))
model_2.add(Activation('relu'))
model_2.add(Dropout(0.2))
model_2.add(Dense(128,kernel_regularizer=regularizers.l1_l2(.001)))
model_2.add(Activation('relu'))
model_2.add(Dropout(0.3))
model_2.add(Dense(32, kernel_regularizer=regularizers.l2(.001)))
model_2.add(Activation('relu'))
model_2.add(Dense(3, activation = 'softmax'))
model_2.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_53 (Dense) (None, 1024) 140288 _________________________________________________________________ activation_42 (Activation) (None, 1024) 0 _________________________________________________________________ dropout_32 (Dropout) (None, 1024) 0 _________________________________________________________________ dense_54 (Dense) (None, 512) 524800 _________________________________________________________________ activation_43 (Activation) (None, 512) 0 _________________________________________________________________ dropout_33 (Dropout) (None, 512) 0 _________________________________________________________________ dense_55 (Dense) (None, 128) 65664 _________________________________________________________________ activation_44 (Activation) (None, 128) 0 _________________________________________________________________ dropout_34 (Dropout) (None, 128) 0 _________________________________________________________________ dense_56 (Dense) (None, 32) 4128 _________________________________________________________________ activation_45 (Activation) (None, 32) 0 _________________________________________________________________ dense_57 (Dense) (None, 3) 99 ================================================================= Total params: 734,979 Trainable params: 734,979 Non-trainable params: 0 _________________________________________________________________
# compile the model_2
model_2.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# checkpoint for model_2
filepath="weights-improvement-best-model_2.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks_list_1 = [checkpoint]
# training the model
start_time_2 = time()
model_2.fit(X_train,y_train,batch_size= 1000, epochs= 100,
validation_data=(X_valid,y_valid),
#validation_split = 0.2,
verbose =2,callbacks = callbacks_list_1)
end_time_2 = time()
Train on 40392 samples, validate on 10098 samples Epoch 1/100 - 4s - loss: 4.7386 - acc: 0.6953 - val_loss: 2.7434 - val_acc: 0.7546 Epoch 00001: val_loss improved from inf to 2.74337, saving model to weights-improvement-best-model_2.hdf5 Epoch 2/100 - 3s - loss: 1.8369 - acc: 0.7472 - val_loss: 1.1403 - val_acc: 0.7604 Epoch 00002: val_loss improved from 2.74337 to 1.14033, saving model to weights-improvement-best-model_2.hdf5 Epoch 3/100 - 3s - loss: 0.9244 - acc: 0.7516 - val_loss: 0.7856 - val_acc: 0.7627 Epoch 00003: val_loss improved from 1.14033 to 0.78558, saving model to weights-improvement-best-model_2.hdf5 Epoch 4/100 - 3s - loss: 0.7643 - acc: 0.7545 - val_loss: 0.7230 - val_acc: 0.7645 Epoch 00004: val_loss improved from 0.78558 to 0.72303, saving model to weights-improvement-best-model_2.hdf5 Epoch 5/100 - 3s - loss: 0.7249 - acc: 0.7569 - val_loss: 0.7024 - val_acc: 0.7645 Epoch 00005: val_loss improved from 0.72303 to 0.70244, saving model to weights-improvement-best-model_2.hdf5 Epoch 6/100 - 3s - loss: 0.7095 - acc: 0.7586 - val_loss: 0.6890 - val_acc: 0.7671 Epoch 00006: val_loss improved from 0.70244 to 0.68902, saving model to weights-improvement-best-model_2.hdf5 Epoch 7/100 - 3s - loss: 0.6974 - acc: 0.7600 - val_loss: 0.6795 - val_acc: 0.7683 Epoch 00007: val_loss improved from 0.68902 to 0.67950, saving model to weights-improvement-best-model_2.hdf5 Epoch 8/100 - 3s - loss: 0.6861 - acc: 0.7617 - val_loss: 0.6728 - val_acc: 0.7645 Epoch 00008: val_loss improved from 0.67950 to 0.67283, saving model to weights-improvement-best-model_2.hdf5 Epoch 9/100 - 3s - loss: 0.6775 - acc: 0.7648 - val_loss: 0.6667 - val_acc: 0.7704 Epoch 00009: val_loss improved from 0.67283 to 0.66671, saving model to weights-improvement-best-model_2.hdf5 Epoch 10/100 - 3s - loss: 0.6724 - acc: 0.7664 - val_loss: 0.6622 - val_acc: 0.7707 Epoch 00010: val_loss improved from 0.66671 to 0.66225, saving model to weights-improvement-best-model_2.hdf5 Epoch 11/100 - 3s - loss: 0.6685 - acc: 0.7672 - val_loss: 0.6586 - val_acc: 0.7728 Epoch 00011: val_loss improved from 0.66225 to 0.65859, saving model to weights-improvement-best-model_2.hdf5 Epoch 12/100 - 3s - loss: 0.6631 - acc: 0.7707 - val_loss: 0.6571 - val_acc: 0.7716 Epoch 00012: val_loss improved from 0.65859 to 0.65707, saving model to weights-improvement-best-model_2.hdf5 Epoch 13/100 - 3s - loss: 0.6584 - acc: 0.7716 - val_loss: 0.6565 - val_acc: 0.7725 Epoch 00013: val_loss improved from 0.65707 to 0.65646, saving model to weights-improvement-best-model_2.hdf5 Epoch 14/100 - 3s - loss: 0.6549 - acc: 0.7722 - val_loss: 0.6553 - val_acc: 0.7685 Epoch 00014: val_loss improved from 0.65646 to 0.65529, saving model to weights-improvement-best-model_2.hdf5 Epoch 15/100 - 3s - loss: 0.6520 - acc: 0.7733 - val_loss: 0.6522 - val_acc: 0.7686 Epoch 00015: val_loss improved from 0.65529 to 0.65219, saving model to weights-improvement-best-model_2.hdf5 Epoch 16/100 - 3s - loss: 0.6499 - acc: 0.7731 - val_loss: 0.6491 - val_acc: 0.7726 Epoch 00016: val_loss improved from 0.65219 to 0.64907, saving model to weights-improvement-best-model_2.hdf5 Epoch 17/100 - 3s - loss: 0.6462 - acc: 0.7757 - val_loss: 0.6537 - val_acc: 0.7731 Epoch 00017: val_loss did not improve from 0.64907 Epoch 18/100 - 3s - loss: 0.6452 - acc: 0.7762 - val_loss: 0.6455 - val_acc: 0.7713 Epoch 00018: val_loss improved from 0.64907 to 0.64546, saving model to weights-improvement-best-model_2.hdf5 Epoch 19/100 - 3s - loss: 0.6410 - acc: 0.7803 - val_loss: 0.6472 - val_acc: 0.7748 Epoch 00019: val_loss did not improve from 0.64546 Epoch 20/100 - 3s - loss: 0.6383 - acc: 0.7802 - val_loss: 0.6473 - val_acc: 0.7766 Epoch 00020: val_loss did not improve from 0.64546 Epoch 21/100 - 3s - loss: 0.6362 - acc: 0.7798 - val_loss: 0.6400 - val_acc: 0.7765 Epoch 00021: val_loss improved from 0.64546 to 0.64004, saving model to weights-improvement-best-model_2.hdf5 Epoch 22/100 - 3s - loss: 0.6354 - acc: 0.7825 - val_loss: 0.6409 - val_acc: 0.7752 Epoch 00022: val_loss did not improve from 0.64004 Epoch 23/100 - 3s - loss: 0.6316 - acc: 0.7840 - val_loss: 0.6389 - val_acc: 0.7801 Epoch 00023: val_loss improved from 0.64004 to 0.63894, saving model to weights-improvement-best-model_2.hdf5 Epoch 24/100 - 3s - loss: 0.6323 - acc: 0.7849 - val_loss: 0.6399 - val_acc: 0.7784 Epoch 00024: val_loss did not improve from 0.63894 Epoch 25/100 - 3s - loss: 0.6308 - acc: 0.7873 - val_loss: 0.6446 - val_acc: 0.7771 Epoch 00025: val_loss did not improve from 0.63894 Epoch 26/100 - 3s - loss: 0.6255 - acc: 0.7861 - val_loss: 0.6396 - val_acc: 0.7742 Epoch 00026: val_loss did not improve from 0.63894 Epoch 27/100 - 3s - loss: 0.6256 - acc: 0.7867 - val_loss: 0.6373 - val_acc: 0.7819 Epoch 00027: val_loss improved from 0.63894 to 0.63731, saving model to weights-improvement-best-model_2.hdf5 Epoch 28/100 - 3s - loss: 0.6246 - acc: 0.7903 - val_loss: 0.6418 - val_acc: 0.7790 Epoch 00028: val_loss did not improve from 0.63731 Epoch 29/100 - 3s - loss: 0.6263 - acc: 0.7887 - val_loss: 0.6422 - val_acc: 0.7831 Epoch 00029: val_loss did not improve from 0.63731 Epoch 30/100 - 3s - loss: 0.6214 - acc: 0.7906 - val_loss: 0.6363 - val_acc: 0.7822 Epoch 00030: val_loss improved from 0.63731 to 0.63635, saving model to weights-improvement-best-model_2.hdf5 Epoch 31/100 - 3s - loss: 0.6184 - acc: 0.7921 - val_loss: 0.6373 - val_acc: 0.7823 Epoch 00031: val_loss did not improve from 0.63635 Epoch 32/100 - 3s - loss: 0.6166 - acc: 0.7956 - val_loss: 0.6399 - val_acc: 0.7785 Epoch 00032: val_loss did not improve from 0.63635 Epoch 33/100 - 3s - loss: 0.6156 - acc: 0.7917 - val_loss: 0.6389 - val_acc: 0.7807 Epoch 00033: val_loss did not improve from 0.63635 Epoch 34/100 - 3s - loss: 0.6156 - acc: 0.7937 - val_loss: 0.6335 - val_acc: 0.7830 Epoch 00034: val_loss improved from 0.63635 to 0.63350, saving model to weights-improvement-best-model_2.hdf5 Epoch 35/100 - 3s - loss: 0.6130 - acc: 0.7959 - val_loss: 0.6370 - val_acc: 0.7839 Epoch 00035: val_loss did not improve from 0.63350 Epoch 36/100 - 3s - loss: 0.6160 - acc: 0.7951 - val_loss: 0.6337 - val_acc: 0.7807 Epoch 00036: val_loss did not improve from 0.63350 Epoch 37/100 - 3s - loss: 0.6129 - acc: 0.7945 - val_loss: 0.6350 - val_acc: 0.7821 Epoch 00037: val_loss did not improve from 0.63350 Epoch 38/100 - 3s - loss: 0.6077 - acc: 0.7980 - val_loss: 0.6348 - val_acc: 0.7832 Epoch 00038: val_loss did not improve from 0.63350 Epoch 39/100 - 3s - loss: 0.6131 - acc: 0.7951 - val_loss: 0.6365 - val_acc: 0.7840 Epoch 00039: val_loss did not improve from 0.63350 Epoch 40/100 - 3s - loss: 0.6096 - acc: 0.7966 - val_loss: 0.6311 - val_acc: 0.7836 Epoch 00040: val_loss improved from 0.63350 to 0.63108, saving model to weights-improvement-best-model_2.hdf5 Epoch 41/100 - 3s - loss: 0.6060 - acc: 0.7990 - val_loss: 0.6357 - val_acc: 0.7834 Epoch 00041: val_loss did not improve from 0.63108 Epoch 42/100 - 3s - loss: 0.6083 - acc: 0.7969 - val_loss: 0.6404 - val_acc: 0.7799 Epoch 00042: val_loss did not improve from 0.63108 Epoch 43/100 - 3s - loss: 0.6079 - acc: 0.7987 - val_loss: 0.6319 - val_acc: 0.7867 Epoch 00043: val_loss did not improve from 0.63108 Epoch 44/100 - 3s - loss: 0.6011 - acc: 0.8004 - val_loss: 0.6356 - val_acc: 0.7834 Epoch 00044: val_loss did not improve from 0.63108 Epoch 45/100 - 3s - loss: 0.6025 - acc: 0.8000 - val_loss: 0.6314 - val_acc: 0.7878 Epoch 00045: val_loss did not improve from 0.63108 Epoch 46/100 - 3s - loss: 0.6037 - acc: 0.8005 - val_loss: 0.6275 - val_acc: 0.7870 Epoch 00046: val_loss improved from 0.63108 to 0.62755, saving model to weights-improvement-best-model_2.hdf5 Epoch 47/100 - 3s - loss: 0.6007 - acc: 0.8017 - val_loss: 0.6392 - val_acc: 0.7806 Epoch 00047: val_loss did not improve from 0.62755 Epoch 48/100 - 3s - loss: 0.6028 - acc: 0.7991 - val_loss: 0.6389 - val_acc: 0.7831 Epoch 00048: val_loss did not improve from 0.62755 Epoch 49/100 - 3s - loss: 0.5993 - acc: 0.8029 - val_loss: 0.6344 - val_acc: 0.7859 Epoch 00049: val_loss did not improve from 0.62755 Epoch 50/100 - 3s - loss: 0.6014 - acc: 0.8007 - val_loss: 0.6295 - val_acc: 0.7858 Epoch 00050: val_loss did not improve from 0.62755 Epoch 51/100 - 3s - loss: 0.5992 - acc: 0.8036 - val_loss: 0.6347 - val_acc: 0.7830 Epoch 00051: val_loss did not improve from 0.62755 Epoch 52/100 - 3s - loss: 0.5967 - acc: 0.8039 - val_loss: 0.6336 - val_acc: 0.7816 Epoch 00052: val_loss did not improve from 0.62755 Epoch 53/100 - 3s - loss: 0.5972 - acc: 0.8033 - val_loss: 0.6362 - val_acc: 0.7859 Epoch 00053: val_loss did not improve from 0.62755 Epoch 54/100 - 3s - loss: 0.5971 - acc: 0.8023 - val_loss: 0.6368 - val_acc: 0.7841 Epoch 00054: val_loss did not improve from 0.62755 Epoch 55/100 - 3s - loss: 0.5984 - acc: 0.8018 - val_loss: 0.6307 - val_acc: 0.7855 Epoch 00055: val_loss did not improve from 0.62755 Epoch 56/100 - 3s - loss: 0.5943 - acc: 0.8044 - val_loss: 0.6306 - val_acc: 0.7873 Epoch 00056: val_loss did not improve from 0.62755 Epoch 57/100 - 3s - loss: 0.5956 - acc: 0.8055 - val_loss: 0.6339 - val_acc: 0.7862 Epoch 00057: val_loss did not improve from 0.62755 Epoch 58/100 - 3s - loss: 0.5934 - acc: 0.8048 - val_loss: 0.6357 - val_acc: 0.7795 Epoch 00058: val_loss did not improve from 0.62755 Epoch 59/100 - 3s - loss: 0.5899 - acc: 0.8086 - val_loss: 0.6326 - val_acc: 0.7893 Epoch 00059: val_loss did not improve from 0.62755 Epoch 60/100 - 3s - loss: 0.5914 - acc: 0.8080 - val_loss: 0.6315 - val_acc: 0.7868 Epoch 00060: val_loss did not improve from 0.62755 Epoch 61/100 - 3s - loss: 0.5913 - acc: 0.8080 - val_loss: 0.6351 - val_acc: 0.7857 Epoch 00061: val_loss did not improve from 0.62755 Epoch 62/100 - 3s - loss: 0.5920 - acc: 0.8075 - val_loss: 0.6375 - val_acc: 0.7832 Epoch 00062: val_loss did not improve from 0.62755 Epoch 63/100 - 3s - loss: 0.5910 - acc: 0.8077 - val_loss: 0.6369 - val_acc: 0.7868 Epoch 00063: val_loss did not improve from 0.62755 Epoch 64/100 - 3s - loss: 0.5902 - acc: 0.8081 - val_loss: 0.6314 - val_acc: 0.7870 Epoch 00064: val_loss did not improve from 0.62755 Epoch 65/100 - 3s - loss: 0.5871 - acc: 0.8094 - val_loss: 0.6318 - val_acc: 0.7850 Epoch 00065: val_loss did not improve from 0.62755 Epoch 66/100 - 3s - loss: 0.5860 - acc: 0.8113 - val_loss: 0.6305 - val_acc: 0.7903 Epoch 00066: val_loss did not improve from 0.62755 Epoch 67/100 - 3s - loss: 0.5869 - acc: 0.8099 - val_loss: 0.6307 - val_acc: 0.7890 Epoch 00067: val_loss did not improve from 0.62755 Epoch 68/100 - 3s - loss: 0.5845 - acc: 0.8109 - val_loss: 0.6411 - val_acc: 0.7841 Epoch 00068: val_loss did not improve from 0.62755 Epoch 69/100 - 3s - loss: 0.5864 - acc: 0.8092 - val_loss: 0.6371 - val_acc: 0.7898 Epoch 00069: val_loss did not improve from 0.62755 Epoch 70/100 - 3s - loss: 0.5850 - acc: 0.8116 - val_loss: 0.6301 - val_acc: 0.7883 Epoch 00070: val_loss did not improve from 0.62755 Epoch 71/100 - 3s - loss: 0.5822 - acc: 0.8122 - val_loss: 0.6317 - val_acc: 0.7876 Epoch 00071: val_loss did not improve from 0.62755 Epoch 72/100 - 3s - loss: 0.5818 - acc: 0.8133 - val_loss: 0.6328 - val_acc: 0.7895 Epoch 00072: val_loss did not improve from 0.62755 Epoch 73/100 - 3s - loss: 0.5810 - acc: 0.8133 - val_loss: 0.6326 - val_acc: 0.7862 Epoch 00073: val_loss did not improve from 0.62755 Epoch 74/100 - 3s - loss: 0.5802 - acc: 0.8144 - val_loss: 0.6296 - val_acc: 0.7876 Epoch 00074: val_loss did not improve from 0.62755 Epoch 75/100 - 3s - loss: 0.5811 - acc: 0.8128 - val_loss: 0.6306 - val_acc: 0.7909 Epoch 00075: val_loss did not improve from 0.62755 Epoch 76/100 - 3s - loss: 0.5774 - acc: 0.8166 - val_loss: 0.6420 - val_acc: 0.7786 Epoch 00076: val_loss did not improve from 0.62755 Epoch 77/100 - 3s - loss: 0.5811 - acc: 0.8131 - val_loss: 0.6331 - val_acc: 0.7875 Epoch 00077: val_loss did not improve from 0.62755 Epoch 78/100 - 3s - loss: 0.5788 - acc: 0.8144 - val_loss: 0.6375 - val_acc: 0.7864 Epoch 00078: val_loss did not improve from 0.62755 Epoch 79/100 - 3s - loss: 0.5773 - acc: 0.8154 - val_loss: 0.6368 - val_acc: 0.7895 Epoch 00079: val_loss did not improve from 0.62755 Epoch 80/100 - 3s - loss: 0.5802 - acc: 0.8133 - val_loss: 0.6348 - val_acc: 0.7826 Epoch 00080: val_loss did not improve from 0.62755 Epoch 81/100 - 3s - loss: 0.5771 - acc: 0.8154 - val_loss: 0.6398 - val_acc: 0.7827 Epoch 00081: val_loss did not improve from 0.62755 Epoch 82/100 - 3s - loss: 0.5803 - acc: 0.8131 - val_loss: 0.6358 - val_acc: 0.7855 Epoch 00082: val_loss did not improve from 0.62755 Epoch 83/100 - 3s - loss: 0.5779 - acc: 0.8125 - val_loss: 0.6364 - val_acc: 0.7849 Epoch 00083: val_loss did not improve from 0.62755 Epoch 84/100 - 3s - loss: 0.5759 - acc: 0.8142 - val_loss: 0.6368 - val_acc: 0.7900 Epoch 00084: val_loss did not improve from 0.62755 Epoch 85/100 - 3s - loss: 0.5756 - acc: 0.8157 - val_loss: 0.6375 - val_acc: 0.7806 Epoch 00085: val_loss did not improve from 0.62755 Epoch 86/100 - 3s - loss: 0.5728 - acc: 0.8167 - val_loss: 0.6361 - val_acc: 0.7918 Epoch 00086: val_loss did not improve from 0.62755 Epoch 87/100 - 3s - loss: 0.5729 - acc: 0.8176 - val_loss: 0.6373 - val_acc: 0.7869 Epoch 00087: val_loss did not improve from 0.62755 Epoch 88/100 - 3s - loss: 0.5706 - acc: 0.8198 - val_loss: 0.6388 - val_acc: 0.7819 Epoch 00088: val_loss did not improve from 0.62755 Epoch 89/100 - 3s - loss: 0.5741 - acc: 0.8173 - val_loss: 0.6372 - val_acc: 0.7906 Epoch 00089: val_loss did not improve from 0.62755 Epoch 90/100 - 3s - loss: 0.5722 - acc: 0.8178 - val_loss: 0.6344 - val_acc: 0.7888 Epoch 00090: val_loss did not improve from 0.62755 Epoch 91/100 - 3s - loss: 0.5710 - acc: 0.8179 - val_loss: 0.6392 - val_acc: 0.7880 Epoch 00091: val_loss did not improve from 0.62755 Epoch 92/100 - 3s - loss: 0.5701 - acc: 0.8190 - val_loss: 0.6339 - val_acc: 0.7886 Epoch 00092: val_loss did not improve from 0.62755 Epoch 93/100 - 3s - loss: 0.5719 - acc: 0.8196 - val_loss: 0.6418 - val_acc: 0.7818 Epoch 00093: val_loss did not improve from 0.62755 Epoch 94/100 - 3s - loss: 0.5719 - acc: 0.8165 - val_loss: 0.6316 - val_acc: 0.7914 Epoch 00094: val_loss did not improve from 0.62755 Epoch 95/100 - 3s - loss: 0.5697 - acc: 0.8205 - val_loss: 0.6347 - val_acc: 0.7861 Epoch 00095: val_loss did not improve from 0.62755 Epoch 96/100 - 3s - loss: 0.5694 - acc: 0.8189 - val_loss: 0.6400 - val_acc: 0.7903 Epoch 00096: val_loss did not improve from 0.62755 Epoch 97/100 - 3s - loss: 0.5672 - acc: 0.8195 - val_loss: 0.6372 - val_acc: 0.7873 Epoch 00097: val_loss did not improve from 0.62755 Epoch 98/100 - 3s - loss: 0.5682 - acc: 0.8186 - val_loss: 0.6356 - val_acc: 0.7891 Epoch 00098: val_loss did not improve from 0.62755 Epoch 99/100 - 3s - loss: 0.5664 - acc: 0.8196 - val_loss: 0.6402 - val_acc: 0.7879 Epoch 00099: val_loss did not improve from 0.62755 Epoch 100/100 - 3s - loss: 0.5688 - acc: 0.8189 - val_loss: 0.6349 - val_acc: 0.7900 Epoch 00100: val_loss did not improve from 0.62755
# Evaluating the model on the training and testing set
score = model.evaluate(X_train, y_train)
print("\n Training Accuracy on model_1:", score[1])
score = model.evaluate(X_test, y_test)
print("\n Testing Accuracy on model_1:", score[1])
40392/40392 [==============================] - 2s 58us/step Training Accuracy on model_1: 0.8096157654981184 8910/8910 [==============================] - 1s 57us/step Testing Accuracy on model_1: 0.7802469135534884
# Evaluating the model_1 on the training and testing set
score = model_1.evaluate(X_train, y_train)
print("\n Training Accuracy on model_1:", score[1])
score = model_1.evaluate(X_test, y_test)
print("\n Testing Accuracy on model_1:", score[1])
40392/40392 [==============================] - 3s 75us/step Training Accuracy on model_1: 0.8573232323232324 8910/8910 [==============================] - 1s 79us/step Testing Accuracy on model_1: 0.7858585858318273
# Evaluating the model_2 on the training and testing set
score = model_2.evaluate(X_train, y_train)
print("\n Training Accuracy on model_1:", score[1])
score = model_2.evaluate(X_test, y_test)
print("\n Testing Accuracy on model_1:", score[1])
40392/40392 [==============================] - 2s 58us/step Training Accuracy on model_1: 0.8356110120816003 8910/8910 [==============================] - 1s 57us/step Testing Accuracy on model_1: 0.7922558922291337